Digital modeling and tracking of agricultural fields for implementing agricultural field trials

ABSTRACT

A system for implementing a trial in one or more fields is provided. In an embodiment, an agricultural intelligence computing system receives field data for a plurality of agricultural fields. Based, at least in part, on the field data for the plurality of agricultural fields, the agricultural intelligence computing system identifies one or more target agricultural fields. The agricultural intelligence computing system sends, to a field manager computing device associated with the one or more target agricultural fields, a trial participation request. The server receives data indicating acceptance of the trial participation request from the field manager computing device. The server determines one or more locations on the one or more target agricultural fields for implementing a trial and sends data identifying the one or more locations to the field manager computing device. When the agricultural intelligence computing system receives application data for the one or more target agricultural fields, the agricultural intelligence computing system determines whether the one or more target agricultural fields are in compliance with the trial. The agricultural intelligence computing system then receives result data for the trial and, based on the result data, computes a benefit value for the trial.

BENEFIT CLAIM

This application claims the benefit under 35 U.S.C. § 119 of application62/808,807, filed Feb. 21, 2019, the entire contents of which areincorporated by reference for all purposes as if fully set forth herein.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyright orrights whatsoever. © 2020 The Climate Corporation.

FIELD OF THE DISCLOSURE

The present disclosure relates to digital computer modeling and trackingof agricultural fields. Specifically, the present disclosure relates tomodeling benefits to an agricultural field of performing particularpractices, identifying locations for implementing trials of theparticular practices and tracking the performance of the particularpractices.

BACKGROUND

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

Field managers are faced with a wide variety of decisions to make withrespect to the management of agricultural fields. These decisions rangefrom determining what crop to plant, which type of seed to plant for thecrop, when to harvest a crop, whether to perform tillage, irrigation,application of pesticides, application of fungicides, and application offertilizer, and what types of pesticides, fungicides, and fertilizers toapply.

Often, improvements may be made to the management practices of a fieldby using different hybrid seeds, applying different products to thefield, or performing different management activities on the field. Theseimprovements may not be readily identifiable to a field manager workingwith only information about their own field. Thus, it is beneficial fora computer system which obtains information regarding a plurality offields to identify improvements to planting practices, managementpractices, or application practices.

While recommended improvements may be useful for agricultural fields,they can be risky to implement. Where a field manager can feel assuredthat the field manager's practices will produce a particular result, thefield manager may not feel assured that following the recommendationwould lead to a benefit.

Even if a field manager agrees to follow a recommendation, the fieldmanager would not be able to quantify whether benefits achieved are dueto the different planting, application, or management practices or dueto one or more outside factors such as favorable weather. Thus, withoutbeing able to quantify the benefits of particular new practices, a fieldmanager is unable to determine whether the practices should be used infuture years.

Thus, there is a need for a method of identifying fields that couldbenefit from changes in agricultural practices and developing trialsthat can demonstrate the value in the changes to the agriculturalpractices.

SUMMARY

The appended claims may serve as a summary of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 illustrates an example computer system that is configured toperform the functions described herein, shown in a field environmentwith other apparatus with which the system may interoperate.

FIG. 2 illustrates two views of an example logical organization of setsof instructions in main memory when an example mobile application isloaded for execution.

FIG. 3 illustrates a programmed process by which the agriculturalintelligence computer system generates one or more preconfiguredagronomic models using agronomic data provided by one or more datasources.

FIG. 4 is a block diagram that illustrates a computer system upon whichan embodiment of the invention may be implemented.

FIG. 5 depicts an example embodiment of a timeline view for data entry.

FIG. 6 depicts an example embodiment of a spreadsheet view for dataentry.

FIG. 7 depicts an example method of implementing a trial. At step 702,field data for a plurality of agricultural fields is received.

FIG. 8 depicts an example of implementing testing locations on a field.

FIG. 9 depicts a graphical user interface for selecting locations toplace testing locations.

FIG. 10 depicts an example graphical user interface for definingselected locations.

FIG. 11 depicts an example graphical user interface for displayinginformation pertaining to a selected region.

FIG. 12 depicts an example graphical user interface for depictingresults of a trial.

FIG. 13 illustrates an example process performed by the field studyserver from field targeting to information distribution across growersystems.

FIG. 14 illustrates an example relationship between the crop density andthe crop yield for a given hybrid.

FIG. 15 illustrates example types of management practice.

FIG. 16 illustrates an example process performed by the field studyserver to determine the crop hybrid for a grower's field or the zonesthereof.

FIG. 17 illustrates an example process performed by the field studyserver of targeting grower fields for crop yield lift.

FIG. 18 depicts an example data flow for producing one or moreoutcome-based values for a recommendation.

FIG. 19 depicts an example outcome-based display.

FIG. 20 depicts a method for modeling short length variability within afield.

FIG. 21 depicts a selection of a plurality of sets of adjacent cells.

FIG. 22 depicts an example method of varying testing locations within apreset grid to maximize a number of testing locations.

FIG. 23 depicts an example method of implementing a trial on anagricultural field.

FIG. 24 is an example of data layers that may be stored for anagricultural field.

FIG. 25 depicts an example method for augmenting a graphical userinterface based on past yield response data for an agricultural field.

FIG. 26 depicts an example of generating upper and lower bounds for adensity curve.

FIG. 27 depicts an example graphical user interface for modifying atrial.

FIG. 28 comprises an example method for communicating with a fieldmanager computing device regarding the implementation of a trial subjectto one or more rules.

FIG. 29 depicts an example GUI displaying a plurality of trialrecommendations.

FIG. 30 depicts an example GUI displaying a particular trialrecommendation.

FIG. 31 depicts an example GUI displaying a comparison of outcome-basedvalues.

FIG. 32 depicts an example GUI displaying information relating to the“Seeds By Acre” outcome based value.

FIG. 33 depicts an example GUI displaying information relating to the“Performance Guarantee” outcome-based value.

FIG. 34 depicts an example GUI displaying information relating to the“Performance Partner” outcome-based value.

FIG. 35 depicts an example GUI displaying information relating to the“Profit Partner” outcome-based value.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present disclosure. It will be apparent, however,that embodiments may be practiced without these specific details. Inother instances, well-known structures and devices are shown in blockdiagram form in order to avoid unnecessarily obscuring the presentdisclosure. Embodiments are disclosed in sections according to thefollowing outline:

1. GENERAL OVERVIEW

2. EXAMPLE AGRICULTURAL INTELLIGENCE COMPUTER SYSTEM

-   -   2.1. STRUCTURAL OVERVIEW    -   2.2. APPLICATION PROGRAM OVERVIEW    -   2.3. DATA INGEST TO THE COMPUTER SYSTEM    -   2.4. PROCESS OVERVIEW—AGRONOMIC MODEL TRAINING    -   2.5. IMPLEMENTATION EXAMPLE—HARDWARE OVERVIEW

3. FUNCTIONAL OVERVIEW

4. PROVIDED FIELD DATA

5. TARGET IDENTIFICATION

6. TRIAL DESIGN

7. FIELD MANAGER COMPUTING DEVICE COMMUNICATION

8. VALUE ASSOCIATION

9. OUTCOME BASED IMPLEMENTATION

-   -   9.1. VALUE TYPES    -   9.2. DATA FLOW    -   9.3. YIELD MODELING TO GENERATE GUARANTEE VALUES    -   9.4. EXAMPLE OUTCOME-BASED DISPLAY    -   9.5. EXAMPLE OUTCOME-BASED TRIAL GENERATION    -   9.6. EXAMPLE TRIAL RECOMMENDATION VARIATION IMPLEMENTATION    -   9.7. EXAMPLE TRIAL BASED OUTCOME COMMUNICATION PROCESS

10. BENEFITS OF CERTAIN EMBODIMENTS

11. EXTENSIONS AND ALTERNATIVES

1. General Overview

Systems and methods for implementing trials in one or more fields aredescribed herein. In an embodiment, an agricultural intelligencecomputer system is communicatively coupled to a plurality of fieldmanager computing devices. The agricultural intelligence computer systemreceives field data for a plurality of agricultural fields and uses thefield data to identify fields which would benefit from performing aparticular trial. The agricultural intelligence computer system sends atrial participation request to a field manager computing deviceassociated with an identified field which guarantees a particularbenefit for participating in the trial. If the field manager computingdevice agrees to participate in the trial, the agricultural intelligencecomputer system identifies locations on the identified field forimplementing the trial and sends the data to the field manager computingdevice. The agricultural intelligence computer system may trackpractices on the identified field to determine whether the identifiedfield is in compliance with the trial. The agricultural intelligencecomputer system may additionally receive data identifying results of thetrial and use the data to compute one or more benefits of the trial.

In an embodiment, a method comprises receiving, at an agriculturalintelligence computer system, field data for a plurality of agriculturalfields; based, at least in part, on the field data for the plurality ofagricultural fields, identifying one or more target agricultural fields;sending, to a field manager computing device associated with the one ormore target agricultural fields, a trial participation request;receiving, from the field manager computing device, data indicatingacceptance of the trial participation request; determining one or morelocations on the one or more target agricultural fields for implementinga trial; sending data identifying the one or more locations to the fieldmanager computing device; receiving application data for the one or moretarget agricultural fields; based on the application data, determiningwhether the one or more target agricultural fields are in compliancewith the trial; receiving result data for the trial; based on the resultdata, computing a benefit value for the trial.

2. Example Agricultural Intelligence Computer System

2.1 Structural Overview

FIG. 1 illustrates an example computer system that is configured toperform the functions described herein, shown in a field environmentwith other apparatus with which the system may interoperate. In oneembodiment, a user 102 owns, operates or possesses a field managercomputing device 104 in a field location or associated with a fieldlocation such as a field intended for agricultural activities or amanagement location for one or more agricultural fields. The fieldmanager computer device 104 is programmed or configured to provide fielddata 106 to an agricultural intelligence computer system 130 via one ormore networks 109.

Examples of field data 106 include (a) identification data (for example,acreage, field name, field identifiers, geographic identifiers, boundaryidentifiers, crop identifiers, and any other suitable data that may beused to identify farm land, such as a common land unit (CLU), lot andblock number, a parcel number, geographic coordinates and boundaries,Farm Serial Number (FSN), farm number, tract number, field number,section, township, and/or range), (b) harvest data (for example, croptype, crop variety, crop rotation, whether the crop is grownorganically, harvest date, Actual Production History (APH), expectedyield, yield, crop price, crop revenue, grain moisture, tillagepractice, and previous growing season information), (c) soil data (forexample, type, composition, pH, organic matter (OM), cation exchangecapacity (CEC)), (d) planting data (for example, planting date, seed(s)type, relative maturity (RM) of planted seed(s), seed population), (e)fertilizer data (for example, nutrient type (Nitrogen, Phosphorous,Potassium), application type, application date, amount, source, method),(f) chemical application data (for example, pesticide, herbicide,fungicide, other substance or mixture of substances intended for use asa plant regulator, defoliant, or desiccant, application date, amount,source, method), (g) irrigation data (for example, application date,amount, source, method), (h) weather data (for example, precipitation,rainfall rate, predicted rainfall, water runoff rate region,temperature, wind, forecast, pressure, visibility, clouds, heat index,dew point, humidity, snow depth, air quality, sunrise, sunset), (i)imagery data (for example, imagery and light spectrum information froman agricultural apparatus sensor, camera, computer, smartphone, tablet,unmanned aerial vehicle, planes or satellite), (j) scouting observations(photos, videos, free form notes, voice recordings, voicetranscriptions, weather conditions (temperature, precipitation (currentand over time), soil moisture, crop growth stage, wind velocity,relative humidity, dew point, black layer)), and (k) soil, seed, cropphenology, pest and disease reporting, and predictions sources anddatabases.

A data server computer 108 is communicatively coupled to agriculturalintelligence computer system 130 and is programmed or configured to sendexternal data 110 to agricultural intelligence computer system 130 viathe network(s) 109. The external data server computer 108 may be ownedor operated by the same legal person or entity as the agriculturalintelligence computer system 130, or by a different person or entitysuch as a government agency, non-governmental organization (NGO), and/ora private data service provider. Examples of external data includeweather data, imagery data, soil data, or statistical data relating tocrop yields, among others. External data 110 may consist of the sametype of information as field data 106. In some embodiments, the externaldata 110 is provided by an external data server 108 owned by the sameentity that owns and/or operates the agricultural intelligence computersystem 130. For example, the agricultural intelligence computer system130 may include a data server focused exclusively on a type of data thatmight otherwise be obtained from third party sources, such as weatherdata. In some embodiments, an external data server 108 may actually beincorporated within the system 130.

An agricultural apparatus 111 may have one or more remote sensors 112fixed thereon, which sensors are communicatively coupled either directlyor indirectly via agricultural apparatus 111 to the agriculturalintelligence computer system 130 and are programmed or configured tosend sensor data to agricultural intelligence computer system 130.Examples of agricultural apparatus 111 include tractors, combines,harvesters, planters, trucks, fertilizer equipment, aerial vehiclesincluding unmanned aerial vehicles, and any other item of physicalmachinery or hardware, typically mobile machinery, and which may be usedin tasks associated with agriculture. In some embodiments, a single unitof apparatus 111 may comprise a plurality of sensors 112 that arecoupled locally in a network on the apparatus; controller area network(CAN) is example of such a network that can be installed in combines,harvesters, sprayers, and cultivators. Application controller 114 iscommunicatively coupled to agricultural intelligence computer system 130via the network(s) 109 and is programmed or configured to receive one ormore scripts that are used to control an operating parameter of anagricultural vehicle or implement from the agricultural intelligencecomputer system 130. For instance, a controller area network (CAN) businterface may be used to enable communications from the agriculturalintelligence computer system 130 to the agricultural apparatus 111, suchas how the CLIMATE FIELDVIEW DRIVE, available from The ClimateCorporation, San Francisco, Calif., is used. Sensor data may consist ofthe same type of information as field data 106. In some embodiments,remote sensors 112 may not be fixed to an agricultural apparatus 111 butmay be remotely located in the field and may communicate with network109.

The apparatus 111 may comprise a cab computer 115 that is programmedwith a cab application, which may comprise a version or variant of themobile application for device 104 that is further described in othersections herein. In an embodiment, cab computer 115 comprises a compactcomputer, often a tablet-sized computer or smartphone, with a graphicalscreen display, such as a color display, that is mounted within anoperator's cab of the apparatus 111. Cab computer 115 may implement someor all of the operations and functions that are described further hereinfor the mobile computer device 104.

The network(s) 109 broadly represent any combination of one or more datacommunication networks including local area networks, wide areanetworks, internetworks or internets, using any of wireline or wirelesslinks, including terrestrial or satellite links. The network(s) may beimplemented by any medium or mechanism that provides for the exchange ofdata between the various elements of FIG. 1. The various elements ofFIG. 1 may also have direct (wired or wireless) communications links.The sensors 112, controller 114, external data server computer 108, andother elements of the system each comprise an interface compatible withthe network(s) 109 and are programmed or configured to use standardizedprotocols for communication across the networks such as TCP/IP,Bluetooth, CAN protocol and higher-layer protocols such as HTTP, TLS,and the like.

Agricultural intelligence computer system 130 is programmed orconfigured to receive field data 106 from field manager computing device104, external data 110 from external data server computer 108, andsensor data from remote sensor 112. Agricultural intelligence computersystem 130 may be further configured to host, use or execute one or morecomputer programs, other software elements, digitally programmed logicsuch as FPGAs or ASICs, or any combination thereof to performtranslation and storage of data values, construction of digital modelsof one or more crops on one or more fields, generation ofrecommendations and notifications, and generation and sending of scriptsto application controller 114, in the manner described further in othersections of this disclosure.

In an embodiment, agricultural intelligence computer system 130 isprogrammed with or comprises a communication layer 132, presentationlayer 134, data management layer 140, hardware/virtualization layer 150,and model and field data repository 160. “Layer,” in this context,refers to any combination of electronic digital interface circuits,microcontrollers, firmware such as drivers, and/or computer programs orother software elements.

Communication layer 132 may be programmed or configured to performinput/output interfacing functions including sending requests to fieldmanager computing device 104, external data server computer 108, andremote sensor 112 for field data, external data, and sensor datarespectively. Communication layer 132 may be programmed or configured tosend the received data to model and field data repository 160 to bestored as field data 106.

Presentation layer 134 may be programmed or configured to generate agraphical user interface (GUI) to be displayed on field managercomputing device 104, cab computer 115 or other computers that arecoupled to the system 130 through the network 109. The GUI may comprisecontrols for inputting data to be sent to agricultural intelligencecomputer system 130, generating requests for models and/orrecommendations, and/or displaying recommendations, notifications,models, and other field data.

Data management layer 140 may be programmed or configured to manage readoperations and write operations involving the repository 160 and otherfunctional elements of the system, including queries and result setscommunicated between the functional elements of the system and therepository. Examples of data management layer 140 include JDBC, SQLserver interface code, and/or HADOOP interface code, among others.Repository 160 may comprise a database. As used herein, the term“database” may refer to either a body of data, a relational databasemanagement system (RDBMS), or to both. As used herein, a database maycomprise any collection of data including hierarchical databases,relational databases, flat file databases, object-relational databases,object oriented databases, distributed databases, and any otherstructured collection of records or data that is stored in a computersystem. Examples of RDBMS's include, but are not limited to including,ORACLE®, MYSQL, IBM® DB2, MICROSOFT® SQL SERVER, SYBASE®, and POSTGRESQLdatabases. However, any database may be used that enables the systemsand methods described herein.

When field data 106 is not provided directly to the agriculturalintelligence computer system via one or more agricultural machines oragricultural machine devices that interacts with the agriculturalintelligence computer system, the user may be prompted via one or moreuser interfaces on the user device (served by the agriculturalintelligence computer system) to input such information. In an exampleembodiment, the user may specify identification data by accessing a mapon the user device (served by the agricultural intelligence computersystem) and selecting specific CLUs that have been graphically shown onthe map. In an alternative embodiment, the user 102 may specifyidentification data by accessing a map on the user device (served by theagricultural intelligence computer system 130) and drawing boundaries ofthe field over the map. Such CLU selection or map drawings representgeographic identifiers. In alternative embodiments, the user may specifyidentification data by accessing field identification data (provided asshape files or in a similar format) from the U. S. Department ofAgriculture Farm Service Agency or other source via the user device andproviding such field identification data to the agriculturalintelligence computer system.

In an example embodiment, the agricultural intelligence computer system130 is programmed to generate and cause displaying a graphical userinterface comprising a data manager for data input. After one or morefields have been identified using the methods described above, the datamanager may provide one or more graphical user interface widgets whichwhen selected can identify changes to the field, soil, crops, tillage,or nutrient practices. The data manager may include a timeline view, aspreadsheet view, and/or one or more editable programs.

FIG. 5 depicts an example embodiment of a timeline view for data entry.Using the display depicted in FIG. 5, a user computer can input aselection of a particular field and a particular date for the additionof event. Events depicted at the top of the timeline may includeNitrogen, Planting, Practices, and Soil. To add a nitrogen applicationevent, a user computer may provide input to select the nitrogen tab. Theuser computer may then select a location on the timeline for aparticular field in order to indicate an application of nitrogen on theselected field. In response to receiving a selection of a location onthe timeline for a particular field, the data manager may display a dataentry overlay, allowing the user computer to input data pertaining tonitrogen applications, planting procedures, soil application, tillageprocedures, irrigation practices, or other information relating to theparticular field. For example, if a user computer selects a portion ofthe timeline and indicates an application of nitrogen, then the dataentry overlay may include fields for inputting an amount of nitrogenapplied, a date of application, a type of fertilizer used, and any otherinformation related to the application of nitrogen.

In an embodiment, the data manager provides an interface for creatingone or more programs. “Program,” in this context, refers to a set ofdata pertaining to nitrogen applications, planting procedures, soilapplication, tillage procedures, irrigation practices, or otherinformation that may be related to one or more fields, and that can bestored in digital data storage for reuse as a set in other operations.After a program has been created, it may be conceptually applied to oneor more fields and references to the program may be stored in digitalstorage in association with data identifying the fields. Thus, insteadof manually entering identical data relating to the same nitrogenapplications for multiple different fields, a user computer may create aprogram that indicates a particular application of nitrogen and thenapply the program to multiple different fields. For example, in thetimeline view of FIG. 5, the top two timelines have the “Spring applied”program selected, which includes an application of 150 lbs N/ac in earlyApril. The data manager may provide an interface for editing a program.In an embodiment, when a particular program is edited, each field thathas selected the particular program is edited. For example, in FIG. 5,if the “Spring applied” program is edited to reduce the application ofnitrogen to 130 lbs N/ac, the top two fields may be updated with areduced application of nitrogen based on the edited program.

In an embodiment, in response to receiving edits to a field that has aprogram selected, the data manager removes the correspondence of thefield to the selected program. For example, if a nitrogen application isadded to the top field in FIG. 5, the interface may update to indicatethat the “Spring applied” program is no longer being applied to the topfield. While the nitrogen application in early April may remain, updatesto the “Spring applied” program would not alter the April application ofnitrogen.

FIG. 6 depicts an example embodiment of a spreadsheet view for dataentry. Using the display depicted in FIG. 6, a user can create and editinformation for one or more fields. The data manager may includespreadsheets for inputting information with respect to Nitrogen,Planting, Practices, and Soil as depicted in FIG. 6. To edit aparticular entry, a user computer may select the particular entry in thespreadsheet and update the values. For example, FIG. 6 depicts anin-progress update to a target yield value for the second field.Additionally, a user computer may select one or more fields in order toapply one or more programs. In response to receiving a selection of aprogram for a particular field, the data manager may automaticallycomplete the entries for the particular field based on the selectedprogram. As with the timeline view, the data manager may update theentries for each field associated with a particular program in responseto receiving an update to the program. Additionally, the data managermay remove the correspondence of the selected program to the field inresponse to receiving an edit to one of the entries for the field.

In an embodiment, model and field data is stored in model and field datarepository 160. Model data comprises data models created for one or morefields. For example, a crop model may include a digitally constructedmodel of the development of a crop on the one or more fields. “Model,”in this context, refers to an electronic digitally stored set ofexecutable instructions and data values, associated with one another,which are capable of receiving and responding to a programmatic or otherdigital call, invocation, or request for resolution based upon specifiedinput values, to yield one or more stored or calculated output valuesthat can serve as the basis of computer-implemented recommendations,output data displays, or machine control, among other things. Persons ofskill in the field find it convenient to express models usingmathematical equations, but that form of expression does not confine themodels disclosed herein to abstract concepts; instead, each model hereinhas a practical application in a computer in the form of storedexecutable instructions and data that implement the model using thecomputer. The model may include a model of past events on the one ormore fields, a model of the current status of the one or more fields,and/or a model of predicted events on the one or more fields. Model andfield data may be stored in data structures in memory, rows in adatabase table, in flat files or spreadsheets, or other forms of storeddigital data.

In an embodiment, each of target identification instructions 135, trialdesign instructions 136, trial tracking instructions 137, and valueassociation instructions 138 comprises a set of one or more pages ofmain memory, such as RAM, in the agricultural intelligence computersystem 130 into which executable instructions have been loaded and whichwhen executed cause the agricultural intelligence computing system toperform the functions or operations that are described herein withreference to those modules. For example, the target identificationinstructions 135 may comprise a set of pages in RAM that containinstructions which when executed cause performing the targetidentification functions that are described herein. The instructions maybe in machine executable code in the instruction set of a CPU and mayhave been compiled based upon source code written in JAVA, C, C++,OBJECTIVE-C, or any other human-readable programming language orenvironment, alone or in combination with scripts in JAVASCRIPT, otherscripting languages and other programming source text. The term “pages”is intended to refer broadly to any region within main memory and thespecific terminology used in a system may vary depending on the memoryarchitecture or processor architecture. In another embodiment, each oftarget identification instructions 135, trial design instructions 136,trial tracking instructions 137, and value association instructions 138also may represent one or more files or projects of source code that aredigitally stored in a mass storage device such as non-volatile RAM ordisk storage, in the agricultural intelligence computer system 130 or aseparate repository system, which when compiled or interpreted causegenerating executable instructions which when executed cause theagricultural intelligence computing system to perform the functions oroperations that are described herein with reference to those modules. Inother words, the drawing figure may represent the manner in whichprogrammers or software developers organize and arrange source code forlater compilation into an executable, or interpretation into bytecode orthe equivalent, for execution by the agricultural intelligence computersystem 130.

Target identification instructions 135 comprise computer readableinstructions which, when executed by one or more processors, causeagricultural intelligence computer system 130 to perform identificationof one or more target fields that would benefit from implementing atrial and/or one or more field manager computing devices and/or fieldmanager accounts associated with a field that would benefit fromimplementing a trial. Trial design instructions 136 comprise computerreadable instructions which, when executed by one or more processors,cause agricultural intelligence computer system 130 to performidentification of one or more locations on an agricultural field forimplementing a trial. Trial tracking instructions 137 comprise computerreadable instructions which, when executed by one or more processors,cause agricultural intelligence computer system 130 to perform receivingfield data and determining, based on the field data, whether anagricultural field is in compliance with one or more requirements of atrial. Value association instructions 138 comprise computer readableinstructions which, when executed by one or more processors, causeagricultural intelligence computer system 130 to perform associating avalue with the results of one or more trials.

Hardware/virtualization layer 150 comprises one or more centralprocessing units (CPUs), memory controllers, and other devices,components, or elements of a computer system such as volatile ornon-volatile memory, non-volatile storage such as disk, and I/O devicesor interfaces as illustrated and described, for example, in connectionwith FIG. 4. The layer 150 also may comprise programmed instructionsthat are configured to support virtualization, containerization, orother technologies.

For purposes of illustrating a clear example, FIG. 1 shows a limitednumber of instances of certain functional elements. However, in otherembodiments, there may be any number of such elements. For example,embodiments may use thousands or millions of different mobile computingdevices 104 associated with different users. Further, the system 130and/or external data server computer 108 may be implemented using two ormore processors, cores, clusters, or instances of physical machines orvirtual machines, configured in a discrete location or co-located withother elements in a datacenter, shared computing facility or cloudcomputing facility.

2.2. Application Program Overview

In an embodiment, the implementation of the functions described hereinusing one or more computer programs or other software elements that areloaded into and executed using one or more general-purpose computerswill cause the general-purpose computers to be configured as aparticular machine or as a computer that is specially adapted to performthe functions described herein. Further, each of the flow diagrams thatare described further herein may serve, alone or in combination with thedescriptions of processes and functions in prose herein, as algorithms,plans or directions that may be used to program a computer or logic toimplement the functions that are described. In other words, all theprose text herein, and all the drawing figures, together are intended toprovide disclosure of algorithms, plans or directions that aresufficient to permit a skilled person to program a computer to performthe functions that are described herein, in combination with the skilland knowledge of such a person given the level of skill that isappropriate for inventions and disclosures of this type.

In an embodiment, user 102 interacts with agricultural intelligencecomputer system 130 using field manager computing device 104 configuredwith an operating system and one or more application programs or apps;the field manager computing device 104 also may interoperate with theagricultural intelligence computer system independently andautomatically under program control or logical control and direct userinteraction is not always required. Field manager computing device 104broadly represents one or more of a smart phone, PDA, tablet computingdevice, laptop computer, desktop computer, workstation, or any othercomputing device capable of transmitting and receiving information andperforming the functions described herein. Field manager computingdevice 104 may communicate via a network using a mobile applicationstored on field manager computing device 104, and in some embodiments,the device may be coupled using a cable 113 or connector to the sensor112 and/or controller 114. A particular user 102 may own, operate orpossess and use, in connection with system 130, more than one fieldmanager computing device 104 at a time.

The mobile application may provide client-side functionality, via thenetwork to one or more mobile computing devices. In an exampleembodiment, field manager computing device 104 may access the mobileapplication via a web browser or a local client application or app.Field manager computing device 104 may transmit data to, and receivedata from, one or more front-end servers, using web-based protocols orformats such as HTTP, XML, and/or JSON, or app-specific protocols. In anexample embodiment, the data may take the form of requests and userinformation input, such as field data, into the mobile computing device.In some embodiments, the mobile application interacts with locationtracking hardware and software on field manager computing device 104which determines the location of field manager computing device 104using standard tracking techniques such as multilateration of radiosignals, the global positioning system (GPS), WiFi positioning systems,or other methods of mobile positioning. In some cases, location data orother data associated with the device 104, user 102, and/or useraccount(s) may be obtained by queries to an operating system of thedevice or by requesting an app on the device to obtain data from theoperating system.

In an embodiment, field manager computing device 104 sends field data106 to agricultural intelligence computer system 130 comprising orincluding, but not limited to, data values representing one or more of:a geographical location of the one or more fields, tillage informationfor the one or more fields, crops planted in the one or more fields, andsoil data extracted from the one or more fields. Field manager computingdevice 104 may send field data 106 in response to user input from user102 specifying the data values for the one or more fields. Additionally,field manager computing device 104 may automatically send field data 106when one or more of the data values becomes available to field managercomputing device 104. For example, field manager computing device 104may be communicatively coupled to remote sensor 112 and/or applicationcontroller 114 which include an irrigation sensor and/or irrigationcontroller. In response to receiving data indicating that applicationcontroller 114 released water onto the one or more fields, field managercomputing device 104 may send field data 106 to agriculturalintelligence computer system 130 indicating that water was released onthe one or more fields. Field data 106 identified in this disclosure maybe input and communicated using electronic digital data that iscommunicated between computing devices using parameterized URLs overHTTP, or another suitable communication or messaging protocol.

A commercial example of the mobile application is CLIMATE FIELDVIEW,commercially available from The Climate Corporation, San Francisco,Calif. The CLIMATE FIELDVIEW application, or other applications, may bemodified, extended, or adapted to include features, functions, andprogramming that have not been disclosed earlier than the filing date ofthis disclosure. In one embodiment, the mobile application comprises anintegrated software platform that allows a grower to make fact-baseddecisions for their operation because it combines historical data aboutthe grower's fields with any other data that the grower wishes tocompare. The combinations and comparisons may be performed in real timeand are based upon scientific models that provide potential scenarios topermit the grower to make better, more informed decisions.

FIG. 2 illustrates two views of an example logical organization of setsof instructions in main memory when an example mobile application isloaded for execution. In FIG. 2, each named element represents a regionof one or more pages of RAM or other main memory, or one or more blocksof disk storage or other non-volatile storage, and the programmedinstructions within those regions. In one embodiment, in view (a), amobile computer application 200 comprises account-fields-dataingestion-sharing instructions 202, overview and alert instructions 204,digital map book instructions 206, seeds and planting instructions 208,nitrogen instructions 210, weather instructions 212, field healthinstructions 214, and performance instructions 216.

In one embodiment, a mobile computer application 200 comprises account,fields, data ingestion, sharing instructions 202 which are programmed toreceive, translate, and ingest field data from third party systems viamanual upload or APIs. Data types may include field boundaries, yieldmaps, as-planted maps, soil test results, as-applied maps, and/ormanagement zones, among others. Data formats may include shape files,native data formats of third parties, and/or farm management informationsystem (FMIS) exports, among others. Receiving data may occur via manualupload, e-mail with attachment, external APIs that push data to themobile application, or instructions that call APIs of external systemsto pull data into the mobile application. In one embodiment, mobilecomputer application 200 comprises a data inbox. In response toreceiving a selection of the data inbox, the mobile computer application200 may display a graphical user interface for manually uploading datafiles and importing uploaded files to a data manager.

In one embodiment, digital map book instructions 206 comprise field mapdata layers stored in device memory and are programmed with datavisualization tools and geospatial field notes. This provides growerswith convenient information close at hand for reference, logging andvisual insights into field performance. In one embodiment, overview andalert instructions 204 are programmed to provide an operation-wide viewof what is important to the grower, and timely recommendations to takeaction or focus on particular issues. This permits the grower to focustime on what needs attention, to save time and preserve yield throughoutthe season. In one embodiment, seeds and planting instructions 208 areprogrammed to provide tools for seed selection, hybrid placement, andscript creation, including variable rate (VR) script creation, basedupon scientific models and empirical data. This enables growers tomaximize yield or return on investment through optimized seed purchase,placement and population.

In one embodiment, script generation instructions 205 are programmed toprovide an interface for generating scripts, including variable rate(VR) fertility scripts. The interface enables growers to create scriptsfor field implements, such as nutrient applications, planting, andirrigation. For example, a planting script interface may comprise toolsfor identifying a type of seed for planting. Upon receiving a selectionof the seed type, mobile computer application 200 may display one ormore fields broken into management zones, such as the field map datalayers created as part of digital map book instructions 206. In oneembodiment, the management zones comprise soil zones along with a panelidentifying each soil zone and a soil name, texture, drainage for eachzone, or other field data. Mobile computer application 200 may alsodisplay tools for editing or creating such, such as graphical tools fordrawing management zones, such as soil zones, over a map of one or morefields. Planting procedures may be applied to all management zones ordifferent planting procedures may be applied to different subsets ofmanagement zones. When a script is created, mobile computer application200 may make the script available for download in a format readable byan application controller, such as an archived or compressed format.Additionally, and/or alternatively, a script may be sent directly to cabcomputer 115 from mobile computer application 200 and/or uploaded to oneor more data servers and stored for further use.

In one embodiment, nitrogen instructions 210 are programmed to providetools to inform nitrogen decisions by visualizing the availability ofnitrogen to crops. This enables growers to maximize yield or return oninvestment through optimized nitrogen application during the season.Example programmed functions include displaying images such as SSURGOimages to enable drawing of fertilizer application zones and/or imagesgenerated from subfield soil data, such as data obtained from sensors,at a high spatial resolution (as fine as millimeters or smallerdepending on sensor proximity and resolution); upload of existinggrower-defined zones; providing a graph of plant nutrient availabilityand/or a map to enable tuning application(s) of nitrogen across multiplezones; output of scripts to drive machinery; tools for mass data entryand adjustment; and/or maps for data visualization, among others. “Massdata entry,” in this context, may mean entering data once and thenapplying the same data to multiple fields and/or zones that have beendefined in the system; example data may include nitrogen applicationdata that is the same for many fields and/or zones of the same grower,but such mass data entry applies to the entry of any type of field datainto the mobile computer application 200. For example, nitrogeninstructions 210 may be programmed to accept definitions of nitrogenapplication and practices programs and to accept user input specifyingto apply those programs across multiple fields. “Nitrogen applicationprograms,” in this context, refers to stored, named sets of data thatassociates: a name, color code or other identifier, one or more dates ofapplication, types of material or product for each of the dates andamounts, method of application or incorporation such as injected orbroadcast, and/or amounts or rates of application for each of the dates,crop or hybrid that is the subject of the application, among others.“Nitrogen practices programs,” in this context, refer to stored, namedsets of data that associates: a practices name; a previous crop; atillage system; a date of primarily tillage; one or more previoustillage systems that were used; one or more indicators of applicationtype, such as manure, that were used. Nitrogen instructions 210 also maybe programmed to generate and cause displaying a nitrogen graph, whichindicates projections of plant use of the specified nitrogen and whethera surplus or shortfall is predicted; in some embodiments, differentcolor indicators may signal a magnitude of surplus or magnitude ofshortfall. In one embodiment, a nitrogen graph comprises a graphicaldisplay in a computer display device comprising a plurality of rows,each row associated with and identifying a field; data specifying whatcrop is planted in the field, the field size, the field location, and agraphic representation of the field perimeter; in each row, a timelineby month with graphic indicators specifying each nitrogen applicationand amount at points correlated to month names; and numeric and/orcolored indicators of surplus or shortfall, in which color indicatesmagnitude.

In one embodiment, the nitrogen graph may include one or more user inputfeatures, such as dials or slider bars, to dynamically change thenitrogen planting and practices programs so that a user may optimize hisnitrogen graph. The user may then use his optimized nitrogen graph andthe related nitrogen planting and practices programs to implement one ormore scripts, including variable rate (VR) fertility scripts. Nitrogeninstructions 210 also may be programmed to generate and cause displayinga nitrogen map, which indicates projections of plant use of thespecified nitrogen and whether a surplus or shortfall is predicted; insome embodiments, different color indicators may signal a magnitude ofsurplus or magnitude of shortfall. The nitrogen map may displayprojections of plant use of the specified nitrogen and whether a surplusor shortfall is predicted for different times in the past and the future(such as daily, weekly, monthly or yearly) using numeric and/or coloredindicators of surplus or shortfall, in which color indicates magnitude.In one embodiment, the nitrogen map may include one or more user inputfeatures, such as dials or slider bars, to dynamically change thenitrogen planting and practices programs so that a user may optimize hisnitrogen map, such as to obtain a preferred amount of surplus toshortfall. The user may then use his optimized nitrogen map and therelated nitrogen planting and practices programs to implement one ormore scripts, including variable rate (VR) fertility scripts. In otherembodiments, similar instructions to the nitrogen instructions 210 couldbe used for application of other nutrients (such as phosphorus andpotassium), application of pesticide, and irrigation programs.

In one embodiment, weather instructions 212 are programmed to providefield-specific recent weather data and forecasted weather information.This enables growers to save time and have an efficient integrateddisplay with respect to daily operational decisions.

In one embodiment, field health instructions 214 are programmed toprovide timely remote sensing images highlighting in-season cropvariation and potential concerns. Example programmed functions includecloud checking, to identify possible clouds or cloud shadows;determining nitrogen indices based on field images; graphicalvisualization of scouting layers, including, for example, those relatedto field health, and viewing and/or sharing of scouting notes; and/ordownloading satellite images from multiple sources and prioritizing theimages for the grower, among others.

In one embodiment, performance instructions 216 are programmed toprovide reports, analysis, and insight tools using on-farm data forevaluation, insights and decisions. This enables the grower to seekimproved outcomes for the next year through fact-based conclusions aboutwhy return on investment was at prior levels, and insight intoyield-limiting factors. The performance instructions 216 may beprogrammed to communicate via the network(s) 109 to back-end analyticsprograms executed at agricultural intelligence computer system 130and/or external data server computer 108 and configured to analyzemetrics such as yield, yield differential, hybrid, population, SSURGOzone, soil test properties, or elevation, among others. Programmedreports and analysis may include yield variability analysis, treatmenteffect estimation, benchmarking of yield and other metrics against othergrowers based on anonymized data collected from many growers, or datafor seeds and planting, among others.

Applications having instructions configured in this way may beimplemented for different computing device platforms while retaining thesame general user interface appearance. For example, the mobileapplication may be programmed for execution on tablets, smartphones, orserver computers that are accessed using browsers at client computers.Further, the mobile application as configured for tablet computers orsmartphones may provide a full app experience or a cab app experiencethat is suitable for the display and processing capabilities of cabcomputer 115. For example, referring now to view (b) of FIG. 2, in oneembodiment a cab computer application 220 may comprise maps-cabinstructions 222, remote view instructions 224, data collect andtransfer instructions 226, machine alerts instructions 228, scripttransfer instructions 230, and scouting-cab instructions 232. The codebase for the instructions of view (b) may be the same as for view (a)and executables implementing the code may be programmed to detect thetype of platform on which they are executing and to expose, through agraphical user interface, only those functions that are appropriate to acab platform or full platform. This approach enables the system torecognize the distinctly different user experience that is appropriatefor an in-cab environment and the different technology environment ofthe cab. The maps-cab instructions 222 may be programmed to provide mapviews of fields, farms or regions that are useful in directing machineoperation. The remote view instructions 224 may be programmed to turnon, manage, and provide views of machine activity in real-time or nearreal-time to other computing devices connected to the system 130 viawireless networks, wired connectors or adapters, and the like. The datacollect and transfer instructions 226 may be programmed to turn on,manage, and provide transfer of data collected at sensors andcontrollers to the system 130 via wireless networks, wired connectors oradapters, and the like. The machine alerts instructions 228 may beprogrammed to detect issues with operations of the machine or tools thatare associated with the cab and generate operator alerts. The scripttransfer instructions 230 may be configured to transfer in scripts ofinstructions that are configured to direct machine operations or thecollection of data. The scouting-cab instructions 232 may be programmedto display location-based alerts and information received from thesystem 130 based on the location of the field manager computing device104, agricultural apparatus 111, or sensors 112 in the field and ingest,manage, and provide transfer of location-based scouting observations tothe system 130 based on the location of the agricultural apparatus 111or sensors 112 in the field.

2.3. Data Ingest to the Computer System

In an embodiment, external data server computer 108 stores external data110, including soil data representing soil composition for the one ormore fields and weather data representing temperature and precipitationon the one or more fields. The weather data may include past and presentweather data as well as forecasts for future weather data. In anembodiment, external data server computer 108 comprises a plurality ofservers hosted by different entities. For example, a first server maycontain soil composition data while a second server may include weatherdata. Additionally, soil composition data may be stored in multipleservers. For example, one server may store data representing percentageof sand, silt, and clay in the soil while a second server may store datarepresenting percentage of organic matter (OM) in the soil.

In an embodiment, remote sensor 112 comprises one or more sensors thatare programmed or configured to produce one or more observations. Remotesensor 112 may be aerial sensors, such as satellites, vehicle sensors,planting equipment sensors, tillage sensors, fertilizer or insecticideapplication sensors, harvester sensors, and any other implement capableof receiving data from the one or more fields. In an embodiment,application controller 114 is programmed or configured to receiveinstructions from agricultural intelligence computer system 130.Application controller 114 may also be programmed or configured tocontrol an operating parameter of an agricultural vehicle or implement.For example, an application controller may be programmed or configuredto control an operating parameter of a vehicle, such as a tractor,planting equipment, tillage equipment, fertilizer or insecticideequipment, harvester equipment, or other farm implements such as a watervalve. Other embodiments may use any combination of sensors andcontrollers, of which the following are merely selected examples.

The system 130 may obtain or ingest data under user 102 control, on amass basis from a large number of growers who have contributed data to ashared database system. This form of obtaining data may be termed“manual data ingest” as one or more user-controlled computer operationsare requested or triggered to obtain data for use by the system 130. Asan example, the CLIMATE FIELDVIEW application, commercially availablefrom The Climate Corporation, San Francisco, Calif., may be operated toexport data to system 130 for storing in the repository 160.

For example, seed monitor systems can both control planter apparatuscomponents and obtain planting data, including signals from seed sensorsvia a signal harness that comprises a CAN backbone and point-to-pointconnections for registration and/or diagnostics. Seed monitor systemscan be programmed or configured to display seed spacing, population andother information to the user via the cab computer 115 or other deviceswithin the system 130. Examples are disclosed in U.S. Pat. No. 8,738,243and US Pat. Pub. 20150094916, and the present disclosure assumesknowledge of those other patent disclosures.

Likewise, yield monitor systems may contain yield sensors for harvesterapparatus that send yield measurement data to the cab computer 115 orother devices within the system 130. Yield monitor systems may utilizeone or more remote sensors 112 to obtain grain moisture measurements ina combine or other harvester and transmit these measurements to the uservia the cab computer 115 or other devices within the system 130.

In an embodiment, examples of sensors 112 that may be used with anymoving vehicle or apparatus of the type described elsewhere hereininclude kinematic sensors and position sensors. Kinematic sensors maycomprise any of speed sensors such as radar or wheel speed sensors,accelerometers, or gyros. Position sensors may comprise GPS receivers ortransceivers, or WiFi-based position or mapping apps that are programmedto determine location based upon nearby WiFi hotspots, among others.

In an embodiment, examples of sensors 112 that may be used with tractorsor other moving vehicles include engine speed sensors, fuel consumptionsensors, area counters or distance counters that interact with GPS orradar signals, PTO (power take-off) speed sensors, tractor hydraulicssensors configured to detect hydraulics parameters such as pressure orflow, and/or and hydraulic pump speed, wheel speed sensors or wheelslippage sensors. In an embodiment, examples of controllers 114 that maybe used with tractors include hydraulic directional controllers,pressure controllers, and/or flow controllers; hydraulic pump speedcontrollers; speed controllers or governors; hitch position controllers;or wheel position controllers provide automatic steering.

In an embodiment, examples of sensors 112 that may be used with seedplanting equipment such as planters, drills, or air seeders include seedsensors, which may be optical, electromagnetic, or impact sensors;downforce sensors such as load pins, load cells, pressure sensors; soilproperty sensors such as reflectivity sensors, moisture sensors,electrical conductivity sensors, optical residue sensors, or temperaturesensors; component operating criteria sensors such as planting depthsensors, downforce cylinder pressure sensors, seed disc speed sensors,seed drive motor encoders, seed conveyor system speed sensors, or vacuumlevel sensors; or pesticide application sensors such as optical or otherelectromagnetic sensors, or impact sensors. In an embodiment, examplesof controllers 114 that may be used with such seed planting equipmentinclude: toolbar fold controllers, such as controllers for valvesassociated with hydraulic cylinders; downforce controllers, such ascontrollers for valves associated with pneumatic cylinders, airbags, orhydraulic cylinders, and programmed for applying downforce to individualrow units or an entire planter frame; planting depth controllers, suchas linear actuators; metering controllers, such as electric seed meterdrive motors, hydraulic seed meter drive motors, or swath controlclutches; hybrid selection controllers, such as seed meter drive motors,or other actuators programmed for selectively allowing or preventingseed or an air-seed mixture from delivering seed to or from seed metersor central bulk hoppers; metering controllers, such as electric seedmeter drive motors, or hydraulic seed meter drive motors; seed conveyorsystem controllers, such as controllers for a belt seed deliveryconveyor motor; marker controllers, such as a controller for a pneumaticor hydraulic actuator; or pesticide application rate controllers, suchas metering drive controllers, orifice size or position controllers.

In an embodiment, examples of sensors 112 that may be used with tillageequipment include position sensors for tools such as shanks or discs;tool position sensors for such tools that are configured to detectdepth, gang angle, or lateral spacing; downforce sensors; or draft forcesensors. In an embodiment, examples of controllers 114 that may be usedwith tillage equipment include downforce controllers or tool positioncontrollers, such as controllers configured to control tool depth, gangangle, or lateral spacing.

In an embodiment, examples of sensors 112 that may be used in relationto apparatus for applying fertilizer, insecticide, fungicide and thelike, such as on-planter starter fertilizer systems, subsoil fertilizerapplicators, or fertilizer sprayers, include: fluid system criteriasensors, such as flow sensors or pressure sensors; sensors indicatingwhich spray head valves or fluid line valves are open; sensorsassociated with tanks, such as fill level sensors; sectional orsystem-wide supply line sensors, or row-specific supply line sensors; orkinematic sensors such as accelerometers disposed on sprayer booms. Inan embodiment, examples of controllers 114 that may be used with suchapparatus include pump speed controllers; valve controllers that areprogrammed to control pressure, flow, direction, PWM and the like; orposition actuators, such as for boom height, subsoiler depth, or boomposition.

In an embodiment, examples of sensors 112 that may be used withharvesters include yield monitors, such as impact plate strain gauges orposition sensors, capacitive flow sensors, load sensors, weight sensors,or torque sensors associated with elevators or augers, or optical orother electromagnetic grain height sensors; grain moisture sensors, suchas capacitive sensors; grain loss sensors, including impact, optical, orcapacitive sensors; header operating criteria sensors such as headerheight, header type, deck plate gap, feeder speed, and reel speedsensors; separator operating criteria sensors, such as concaveclearance, rotor speed, shoe clearance, or chaffer clearance sensors;auger sensors for position, operation, or speed; or engine speedsensors. In an embodiment, examples of controllers 114 that may be usedwith harvesters include header operating criteria controllers forelements such as header height, header type, deck plate gap, feederspeed, or reel speed; separator operating criteria controllers forfeatures such as concave clearance, rotor speed, shoe clearance, orchaffer clearance; or controllers for auger position, operation, orspeed.

In an embodiment, examples of sensors 112 that may be used with graincarts include weight sensors, or sensors for auger position, operation,or speed. In an embodiment, examples of controllers 114 that may be usedwith grain carts include controllers for auger position, operation, orspeed.

In an embodiment, examples of sensors 112 and controllers 114 may beinstalled in unmanned aerial vehicle (UAV) apparatus or “drones.” Suchsensors may include cameras with detectors effective for any range ofthe electromagnetic spectrum including visible light, infrared,ultraviolet, near-infrared (NIR), and the like; accelerometers;altimeters; temperature sensors; humidity sensors; pitot tube sensors orother airspeed or wind velocity sensors; battery life sensors; or radaremitters and reflected radar energy detection apparatus; otherelectromagnetic radiation emitters and reflected electromagneticradiation detection apparatus. Such controllers may include guidance ormotor control apparatus, control surface controllers, cameracontrollers, or controllers programmed to turn on, operate, obtain datafrom, manage and configure any of the foregoing sensors. Examples aredisclosed in U.S. patent application Ser. No. 14/831,165 and the presentdisclosure assumes knowledge of that other patent disclosure.

In an embodiment, sensors 112 and controllers 114 may be affixed to soilsampling and measurement apparatus that is configured or programmed tosample soil and perform soil chemistry tests, soil moisture tests, andother tests pertaining to soil. For example, the apparatus disclosed inU.S. Pat. Nos. 8,767,194 and 8,712,148 may be used, and the presentdisclosure assumes knowledge of those patent disclosures.

In an embodiment, sensors 112 and controllers 114 may comprise weatherdevices for monitoring weather conditions of fields. For example, theapparatus disclosed in published international applicationWO2016/176355A1, may be used, and the present disclosure assumesknowledge of that patent disclosure.

2.4. Process Overview-Agronomic Model Training

In an embodiment, the agricultural intelligence computer system 130 isprogrammed or configured to create an agronomic model. In this context,an agronomic model is a data structure in memory of the agriculturalintelligence computer system 130 that comprises field data 106, such asidentification data and harvest data for one or more fields. Theagronomic model may also comprise calculated agronomic properties whichdescribe either conditions which may affect the growth of one or morecrops on a field, or properties of the one or more crops, or both.Additionally, an agronomic model may comprise recommendations based onagronomic factors such as crop recommendations, irrigationrecommendations, planting recommendations, fertilizer recommendations,fungicide recommendations, pesticide recommendations, harvestingrecommendations and other crop management recommendations. The agronomicfactors may also be used to estimate one or more crop related results,such as agronomic yield. The agronomic yield of a crop is an estimate ofquantity of the crop that is produced, or in some examples the revenueor profit obtained from the produced crop.

In an embodiment, the agricultural intelligence computer system 130 mayuse a preconfigured agronomic model to calculate agronomic propertiesrelated to currently received location and crop information for one ormore fields. The preconfigured agronomic model is based upon previouslyprocessed field data, including but not limited to, identification data,harvest data, fertilizer data, and weather data. The preconfiguredagronomic model may have been cross validated to ensure accuracy of themodel. Cross validation may include comparison to ground truthing thatcompares predicted results with actual results on a field, such as acomparison of precipitation estimate with a rain gauge or sensorproviding weather data at the same or nearby location or an estimate ofnitrogen content with a soil sample measurement.

FIG. 3 illustrates a programmed process by which the agriculturalintelligence computer system generates one or more preconfiguredagronomic models using field data provided by one or more data sources.FIG. 3 may serve as an algorithm or instructions for programming thefunctional elements of the agricultural intelligence computer system 130to perform the operations that are now described.

At block 305, the agricultural intelligence computer system 130 isconfigured or programmed to implement agronomic data preprocessing offield data received from one or more data sources. The field datareceived from one or more data sources may be preprocessed for thepurpose of removing noise, distorting effects, and confounding factorswithin the agronomic data including measured outliers that couldadversely affect received field data values. Embodiments of agronomicdata preprocessing may include, but are not limited to, removing datavalues commonly associated with outlier data values, specific measureddata points that are known to unnecessarily skew other data values, datasmoothing, aggregation, or sampling techniques used to remove or reduceadditive or multiplicative effects from noise, and other filtering ordata derivation techniques used to provide clear distinctions betweenpositive and negative data inputs.

At block 310, the agricultural intelligence computer system 130 isconfigured or programmed to perform data subset selection using thepreprocessed field data in order to identify datasets useful for initialagronomic model generation. The agricultural intelligence computersystem 130 may implement data subset selection techniques including, butnot limited to, a genetic algorithm method, an all subset models method,a sequential search method, a stepwise regression method, a particleswarm optimization method, and an ant colony optimization method. Forexample, a genetic algorithm selection technique uses an adaptiveheuristic search algorithm, based on evolutionary principles of naturalselection and genetics, to determine and evaluate datasets within thepreprocessed agronomic data.

At block 315, the agricultural intelligence computer system 130 isconfigured or programmed to implement field dataset evaluation. In anembodiment, a specific field dataset is evaluated by creating anagronomic model and using specific quality thresholds for the createdagronomic model. Agronomic models may be compared and/or validated usingone or more comparison techniques, such as, but not limited to, rootmean square error with leave-one-out cross validation (RMSECV), meanabsolute error, and mean percentage error. For example, RMSECV can crossvalidate agronomic models by comparing predicted agronomic propertyvalues created by the agronomic model against historical agronomicproperty values collected and analyzed. In an embodiment, the agronomicdataset evaluation logic is used as a feedback loop where agronomicdatasets that do not meet configured quality thresholds are used duringfuture data subset selection steps (block 310).

At block 320, the agricultural intelligence computer system 130 isconfigured or programmed to implement agronomic model creation basedupon the cross validated agronomic datasets. In an embodiment, agronomicmodel creation may implement multivariate regression techniques tocreate preconfigured agronomic data models.

At block 325, the agricultural intelligence computer system 130 isconfigured or programmed to store the preconfigured agronomic datamodels for future field data evaluation.

2.5. Implementation Example-Hardware Overview

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be hard-wired to perform thetechniques, or may include digital electronic devices such as one ormore application-specific integrated circuits (ASICs) or fieldprogrammable gate arrays (FPGAs) that are persistently programmed toperform the techniques, or may include one or more general purposehardware processors programmed to perform the techniques pursuant toprogram instructions in firmware, memory, other storage, or acombination. Such special-purpose computing devices may also combinecustom hard-wired logic, ASICs, or FPGAs with custom programming toaccomplish the techniques. The special-purpose computing devices may bedesktop computer systems, portable computer systems, handheld devices,networking devices or any other device that incorporates hard-wiredand/or program logic to implement the techniques.

For example, FIG. 4 is a block diagram that illustrates a computersystem 400 upon which an embodiment of the invention may be implemented.Computer system 400 includes a bus 402 or other communication mechanismfor communicating information, and a hardware processor 404 coupled withbus 402 for processing information. Hardware processor 404 may be, forexample, a general purpose microprocessor.

Computer system 400 also includes a main memory 406, such as a randomaccess memory (RAM) or other dynamic storage device, coupled to bus 402for storing information and instructions to be executed by processor404. Main memory 406 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 404. Such instructions, when stored innon-transitory storage media accessible to processor 404, rendercomputer system 400 into a special-purpose machine that is customized toperform the operations specified in the instructions.

Computer system 400 further includes a read only memory (ROM) 408 orother static storage device coupled to bus 402 for storing staticinformation and instructions for processor 404. A storage device 410,such as a magnetic disk, optical disk, or solid-state drive is providedand coupled to bus 402 for storing information and instructions.

Computer system 400 may be coupled via bus 402 to a display 412, such asa cathode ray tube (CRT), for displaying information to a computer user.An input device 414, including alphanumeric and other keys, is coupledto bus 402 for communicating information and command selections toprocessor 404. Another type of user input device is cursor control 416,such as a mouse, a trackball, or cursor direction keys for communicatingdirection information and command selections to processor 404 and forcontrolling cursor movement on display 412. This input device typicallyhas two degrees of freedom in two axes, a first axis (e.g., x) and asecond axis (e.g., y), that allows the device to specify positions in aplane.

Computer system 400 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 400 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 400 in response to processor 404 executing one or more sequencesof one or more instructions contained in main memory 406. Suchinstructions may be read into main memory 406 from another storagemedium, such as storage device 410. Execution of the sequences ofinstructions contained in main memory 406 causes processor 404 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical disks, magnetic disks, or solid-state drives, suchas storage device 410. Volatile media includes dynamic memory, such asmain memory 406. Common forms of storage media include, for example, afloppy disk, a flexible disk, hard disk, solid-state drive, magnetictape, or any other magnetic data storage medium, a CD-ROM, any otheroptical data storage medium, any physical medium with patterns of holes,a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip orcartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 402. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infrared data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 404 for execution. For example,the instructions may initially be carried on a magnetic disk orsolid-state drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 400 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infrared signal and appropriatecircuitry can place the data on bus 402. Bus 402 carries the data tomain memory 406, from which processor 404 retrieves and executes theinstructions. The instructions received by main memory 406 mayoptionally be stored on storage device 410 either before or afterexecution by processor 404.

Computer system 400 also includes a communication interface 418 coupledto bus 402. Communication interface 418 provides a two-way datacommunication coupling to a network link 420 that is connected to alocal network 422. For example, communication interface 418 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 418 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, communication interface 418sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

Network link 420 typically provides data communication through one ormore networks to other data devices. For example, network link 420 mayprovide a connection through local network 422 to a host computer 424 orto data equipment operated by an Internet Service Provider (ISP) 426.ISP 426 in turn provides data communication services through the worldwide packet data communication network now commonly referred to as the“Internet” 428. Local network 422 and Internet 428 both use electrical,electromagnetic or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 420and through communication interface 418, which carry the digital data toand from computer system 400, are example forms of transmission media.

Computer system 400 can send messages and receive data, includingprogram code, through the network(s), network link 420 and communicationinterface 418. In the Internet example, a server 430 might transmit arequested code for an application program through Internet 428, ISP 426,local network 422 and communication interface 418.

The received code may be executed by processor 404 as it is received,and/or stored in storage device 410, or other non-volatile storage forlater execution.

3. Functional Overview

Systems and methods for implementing trials in one or more fields aredescribed herein. As used herein, a trial refers to performing one ormore different agricultural activities in a portion of an agriculturalfield in order to identify a benefit or detriment of performing the oneor more different agricultural activities. As an example, a subfieldarea may be selected in an agricultural field to implement a fungicidetrial. Within the subfield area, the crops may receive an application offungicide while the rest of the field and/or a different subfield areaon the field does not receive an application of fungicide.Alternatively, the rest of the field may receive the application offungicide while the crops within the subfield area do not. The subfieldareas of the field where the one or more different agriculturalactivities are performed are referred to herein as test locations. Insome embodiments, subfield areas that do not include the differentagricultural activities can also be assigned and referred to as testlocations.

In an embodiment, the portion of the agricultural field comprises awhole field such that the trial comprises a recommendation for one ormore different practices being performed on the agricultural field.Implementations which utilize part or all of the agricultural field aredescribed further herein.

Trials may be performed for testing the efficacy of new products,different management practices, different crops, or any combinationthereof. For example, if a field usually does not receive fungicide, atrial may be designed wherein crops within a selected portion of thefield receive fungicide at one or more times during the development ofthe crop. As another example, if a field usually is conventionallytilled, a trial may be designed wherein a selected portion of the fieldis not tilled. Thus, trials may be implemented for determining whetherto follow management practice recommendations instead of beingconstrained to testing the efficacy of a particular product.Additionally or alternatively, trials may be designed to compare twodifferent types of products, planting rates, equipment, and/or othermanagement practices.

Trials may be constrained by one or more rules. A trial may require oneor more testing locations to be of a particular size and/or placed in aparticular location. For example, the trial may require one or moretesting locations to be placed in an area of the field with comparableconditions to the rest of the field. A testing location, as used herein,refers to an area of an agronomic field that receives one or moredifferent treatments from surrounding areas. Thus, a testing locationmay refer to any shape of land on an agronomic field. Additionally oralternatively, the trial may require one or more testing locations to beplaced in an area of the field with conditions differing from the restof the field and/or areas of the field spanning different types ofconditions. The trial may require one or more different managementpractices to be undertaken in one or more testing locations. Forexample, a trial may require a particular seeding rate as part of a testfor planting a different type of hybrid seed.

FIG. 7 depicts an example method of implementing a trial. At step 702,field data for a plurality of agricultural fields is received at theagricultural intelligence computing system. For example, theagricultural intelligence computing system may track developments onfields associated with a plurality of different field managers. Theserver may receive data for the plurality of fields over a network fromfield manager computing devices, remote sensors, and/or externalcomputing systems. Types of field data and methods of obtaining thefield data are described further herein.

At step 704, one or more target agricultural fields are identifiedbased, at least in part, on the field data for the plurality ofagricultural fields. The agricultural intelligence computing system maybe programmed or configured to directly identify fields and/or toidentify field manager accounts as target accounts for sending a trialrequest message. Generally, the agricultural intelligence computingsystem may select target agricultural fields based on likelihood ofacceptance of the trial, likely benefits to the field of performing thetrial, likelihood of detecting the benefits to the field of performingthe trial, and general applicability of the trial. Methods ofidentifying fields are described further herein.

At step 706, a trial participation request is sent by the agriculturalintelligence computing system to a field manager computing deviceassociated with the one or more target agricultural fields. The trialparticipation request may identify a product and/or one or moremanagement practices to be undertaken as part of the trial. The trialparticipation request may additionally include costs or benefits forparticipating in the trial. Trial participation requests are furtherdescribed herein.

At step 708, data indicating acceptance of the trial participationrequest is received from a field manager computing device. For example,the agricultural intelligence computing system may receive, through agraphical user interface executing on the field manager computingdevice, a selection of an option indicating acceptance of the trialparticipation request.

At step 710, one or more locations on the one or more targetagricultural fields are determined for implementing the trial. Theagricultural intelligence computing system may identify locations on thefield for implementing a test location based on areas in the fieldcapable of performing the trial, efficiency of performing the trial ineach location, applicability of the trial to other locations, and/orbenefit to the field of performing the trial. Methods of determininglocations for implementing the test location are described furtherherein.

At step 712, data identifying the one or more locations is sent to afield manager computing device. For example, the agriculturalintelligence computing system may cause display of a map on a display ofa client computing device where the map identifies one or more testlocations along with data indicating the product and/or managementpractices to be applied to the test location. Additionally oralternatively, the agricultural intelligence computing system maygenerate one or more scripts for a field implement on the one or morefields that causes the field implement to apply the product and/ormanagement practices in the one or more locations. The data may beaccompanied with instructions for implementing the trial. Methods foridentifying the one or more test locations to the field managercomputing device are described further herein.

At step 714, application data for the one or more target agriculturalfields is received by the agricultural intelligence computing system.For example, a field implement and/or remote sensor may measure apopulation rate as planted, an application of pesticide, fungicide,and/or fertilizer, irrigation, tillage, or any other application ofproducts, management methods, or value associated with the growing ofone or more crops. Additionally or alternatively, a field manager mayidentify management, planting, and/or application practices to theagricultural intelligence computing system through a graphical userinterface executing on the field manager computing device.

At step 716, based on the application data, it is determined whether theone or more target agricultural fields are in compliance with the trial.For example, the agricultural intelligence computing system maydetermine whether a test location of an appropriate size has beenimplemented in an appropriate position and with the appropriateplanting, product, and/or management rules. If the one or more targetagricultural fields are not in compliance with the trial, theagricultural intelligence computing system may determine a manner ofupdating the trial to allow the field manager a chance to be incompliance with the trial. For example, if the field manager planted anincorrect population rate in a location selected for the trial, theagricultural intelligence computing system may identify a new locationfor implementing part or all of the trial and send data identifying thenew location to the field manager computing device.

At step 718, a computing system receives result data for the trial. Forexample, if the field is either in compliance with the initial trial orupdated trial, the agricultural intelligence computing system mayreceive yield data and/or profit data for both the one more testlocations and the one or more other portions of the field. Additionallyor alternatively, one or more separate computing devices may perform thesteps of computing yield data and computing benefit values prior tosending the benefit values to the agricultural intelligence computersystem. The result data may be sent by the field manager computingdevice and/or by one or more implements or sensors. For example, asatellite image of the one or more fields may be used to compute totalyield and/or infer crop status for both the one or more fields and thelocation of the test locations.

At step 720, based on the result data, a benefit value for the trial iscomputed. For example, the agricultural intelligence computer system maycompute a benefit value as a function of the result data. The benefitvalue may include a value identifying an increase in yield, an increasein profit, a savings in input cost or time, and/or an increase inquality of the crop. Based on the benefit value, the agriculturalintelligence computing system may determine whether to issue a rebatefor the trial, request additional funds, or otherwise exchange valuewith the field manager associated with the field manager computingdevice.

FIG. 7 depicts one example method of implementing a trial. Otherexamples may include less or more steps. For example, an agriculturalintelligence computing system may perform the steps of FIG. 7 withoutsteps 706 and 708, thereby providing the benefits of the targetidentification, the location identification, and the tracking of thetrial without the interactions with the field manager computing device.As another alternative, the agricultural intelligence computing systemmay send multiple possible trial types to a field manager computingdevice with an option to select one or more of the trial types forimplementing on the field.

A field manager computing device, as used herein, may act as acommunication device between a field manager and the agriculturalintelligence computing system and/or as a controller for a fieldimplement. Thus, the agricultural intelligence computing system may sendinstructions to the field manager computing device which, when executedby the field manager computing device, cause controlling implements on afield to implement a trial and/or gather field data. The directcommunication with the field implement may be used to bypasscommunication with the field manager. For example, in step 712, the dataidentifying the locations for implementing the trial may be sent to afield manager computing device which acts as a controller for a fieldimplement, such as a planter or sprayer, thereby causing the fieldimplement to execute the trial in the identified locations, such as byplanting seeds or spraying a treatment according to a trialprescription. The field manager computing device may include a singlecomputing device that communicates with the agricultural intelligencecomputing system or a plurality of computing devices which communicatedwith the agricultural intelligence computing system at different stepsof the process. For example, a first computing device may receive thetrial participation request in step 706 while a second computing devicereceives the location data in step 712.

4. Provided Field Data

In an embodiment, an agricultural intelligence computing systemcommunicates with a plurality of field manager computing devices over anetwork. Each field manager computing device of the plurality of fieldmanager computing devices may be associated with one or more fields. Forexample, the agricultural intelligence computing system may storeaccount information for a plurality of different user accounts. A fieldmanager computing device may sign into a particular user account tocommunicate with the agricultural intelligence computing system. Theuser account may comprise data identifying one or more fields associatedwith the user account.

The agricultural intelligence computing system may receive data from thefield manager computing devices regarding the one or more fields.Additionally or alternatively, the agricultural intelligence computingsystem may receive information regarding the one or more fieldsassociated with the field manager computing devices from one or moreremote sensors on or about the one or more fields, one or moresatellites, one or more manned or unmanned aerial vehicles (MAVs orUAVs), one or more on-the-go sensors, and/or one or more external dataservers. The data may include field descriptions, soil data, plantingdata, fertility data, harvest and yield data, crop protection data, pestand disease data, irrigation data, tiling data, imagery, weather data,and additional management data.

Field descriptions may refer to a field location, a total acreage of thefield, a shape and boundaries of the field, elevation and topographicvariability of the field, tillage history of the field, crop rotationhistory of the field, disease history of the field, crop protection ofthe field, farm equipment use history of the field, and data regarding afield operator. The field location may be identified using GPScoordinates or any other data that identifies a location of the field.Topographic variability may include differences in elevation as well asslope, curvature, and one or more compound topographic indices of areasof the field. Tillage history may include tillage type, depth, and/ortiming. Crop rotation history may include identification of past cropsplanted at each spot on a field and/or data identifying whether croprotation is regular or irregular. Farm equipment use history may includeidentification of the tilling, planting, application, and harvestingequipment. The field operator data may identify one or more people,operations, or service providers who perform activities on the field.

Soil data may include spatial and/or temporally varying subfield soilmoisture, continuous subfield soil temperature, continuous eddycovariance of water on the field, subfield soil texture including classof soil and/or percentage of sand, silt, and/or clay, subfield soil pH,subfield soil organic matter, subfield soil cation exchange capacity,soil testing data including location of soil collection, date of soilcollection, sampling procedure, date of processing, identification ofprocessing facility, and/or identification of one or more peopleprocessing and/or collecting the soil, additional soil chemistry data,bulk density of the soil, and/or buffer capacity. Soil data may bereceived through input from a field manager computing device, one ormore servers associated with a testing facility, one or more remote orproximal connected sensors, one or more models of soil moisture, soiltemperature, and/or other soil chemical or physical parameters, and/orfrom one or more databases of soil information such as the SSURGO soildatabase.

Planting data may include a crop type, seed product information such ashybrid data, variety, seed treatments, relative maturity, growing degreedays to maturity, disease resistance ratings, and/or standability, depthand row spacing, seed population as planted, seed population asexpected, time and date of planting, spatial indexed seed rate, targetyield, planting equipment data such as type, capabilities, anddimensions, use or nonuse of a seed firmer, starting fertilizer data,replant data, existence of trials and/or other experiments, and shapeand boundaries of planting.

Fertility data may include application dates of fertilizer, type ofmixture applied, application location, amount of application and targetrate, manure composition, application methods, fertilizer applicationequipment data such as type, capabilities, and dimensions, and/or costof application.

Harvest and yield data may include harvest dates and time, yield amountby location and/or field, shell weight for products such as corn, testweight, number of combines used on the field, yield monitor data such ascalibration parameters, speed, and header height, elevator measuredvalues such as load wet mass and moisture, stalk integrity, quantifiedyield loss due to stalk integrity issues, equipment data such as type,capabilities, and dimensions, residue management data such as balingdata, early stand count, lodging data including root lodging and stemfailure, greensnap data, white mold data, yellow flash data, and/orshape and boundaries of harvesting.

Crop protection data may include date and time of application of cropprotection chemicals, application type, chemical makeup of cropprotection chemicals and/or adjuvants, carrier volume, application rateof chemicals, carrier solution rate, application location on the field,method of application, user of in-furrow fertilizer and/or insecticide,equipment data such as type, capabilities, and dimensions, and/or costof application.

Pest and disease data may include subfield pathogen presence in planttissue, residue, and soil, damage type and extent from biotic stresscaused by insects, and/or damage type and extent from biotic stresscaused by pathogens. Extent of damage may be identified as low, medium,or high or as one more numeric ratings. Biotic stress and pathogenpresence may be measured and/or modeled.

Irrigation data may include presence of irrigation, irrigation systemtypes, irrigation times, amount of irrigation, use of fertigation,and/or type and amount of fertigation.

Tiling data may include presence of tiling, tiling system types, tilingsystem maps, tiling system flow conductances, and/or flow rates or fluidlevels in tile lines.

Imagery may include leaf level photographs of foliar disease and stress,leaf-level and field-level photographs of stressed plants, satelliteimagery of a field across one or more visual bands, and/or any otherimages of a location on the field. Imagery of the field may additionallyinclude quantifications of damage assigned to portions of the images.Imagery may be based on visible light and/or light bands outside thevisual spectrum.

Weather data may include historical, current, and/or predicted rainfalldata such as amount of rainfall and location of rainfall, historical,current, and/or predicted temperatures including hourly temperature,temperature maximums and minimums, day time temperatures, and nighttimetemperatures, dewpoint, humidity, wind speed, wind direction, solarradiation and sky cover during daytime hours and during nighttime hours,weather impacts on yield, existence of hail, straight line winds,tornadoes, and/or intense precipitation, and/or depth of freeze duringwinter.

Additional management data may include any additional data relating tothe management and care of the crop such as applications, treatments,and observations. Observations may include observed droughts, observedponding, observed drainage, observed crop cover, and/or observed damageto the crops.

5. Target Identification

Based, at least in part, on data regarding a plurality of fieldsassociated with a plurality of field manager computing devices, theagricultural intelligence computing system may select one or moreparticular fields for performing experimental trials. The agriculturalintelligence computing system may consider factors such as a modeledbenefit to a field of implementing the experimental trials, a historicalrisk tolerance associated with a field, a usefulness of using a field toimplement the experimental trials, a likelihood of detecting a benefitto a field of implementing the experimental trials, operationalcapabilities associated with a field, the use of particular equipment ormachinery with a field, and/or identified existing or previousexperiments on a field. Each of these factors is described furtherherein. Additional methods for identifying target fields are describedin Section 5.1. of the present application and in U.S. Patent Pub. No.2019-0357425A1.

In an embodiment, the agricultural intelligence computing system modelsa benefit to a field of implementing an experimental trial. For example,the agricultural intelligence computing system may identify one or morefields for performing a fungicide application trial. The agriculturalintelligence computing system may identify one or more fields which havebeen damaged by fungus in the past and/or are likely to be damaged byfungus in the future. The agricultural intelligence computing system mayadditionally determine that a yield of the field and/or total profit forthe field would result or be benefited by application of a particularfungicide. The agricultural intelligence computing system mayadditionally determine that a yield and/or profit benefit for the fieldby application of a particular fungicide would likely be detectablebased on the size of the yield and/or profit benefit, the variability ofthe yield and/or profit benefit across the field, and/or the size of thefield and the size of the trial or test regions. Based on thedeterminations, the agricultural intelligence computing system mayidentify the one or more fields as good candidates for the fungicideapplication trial.

The agricultural intelligence computing system may model the benefit tothe field based on the responsiveness of the field and an analysis of aproduct's performance. For example, through different trials of theproduct, the agricultural intelligence computing system may determinethat the product, on average, increases yield for responsive fields by afirst amount and increases yield for non-responsive fields by a secondamount. Responsiveness of fields may be determined based on priorpractices and changes in yield. For example, a more responsive fieldwould have a higher change in yield when management practices changewhile a less responsive field would have a lower variation in yield whenmanagement practices change. An agricultural intelligence computingsystem may determine the responsiveness of different areas for aparticular field based on prior practices, prior yield data, and otherfield data from one or many fields. The agricultural intelligencecomputing system may then determine the effectiveness of applying theproduct to the responsive portions and the non-responsive portions ofthe field.

The agricultural intelligence computing system may identify one or morefields that are at risk of one or more events that may affect cropyield. For example, risk of disease may be based on modeled or measuredsoil moisture, existence of ponding on the field, measured or modeledambient temperatures, measured or modeled ambient humidity, recorded ormodeled crop genetics, recorded or modeled planting date, satelliteimagery of the field, and/or thermal imagery of the field. Examples ofidentifying fields that are at risk of one or more events are describedin Patent Pub. No. 2019-0156437A1 and 2019-0156255A1.

Additionally, the agricultural intelligence computing system mayidentify management practices that increase or decrease the risk of theone or more events. Examples for disease control include use ofirrigation, crop rotation, tillage methods, plant genetics, and plantingrate. Additionally, the agricultural intelligence computing system mayidentify environmental factors that increase or decrease the risk of theone or more events. Examples for disease control include soil organicmatter percentage, soil pH, and other soil nutrient concentrations. Theagricultural intelligence computing system may use the environmentalfactors to determine which fields are at risk and select fields based onthe risk percentage or a computed severity of risked damage.

While embodiments are described with respect to application of specificproducts, fields may additionally be identified based on other possiblebenefits to the field from one or more recommendations. For example, ifthe agricultural intelligence computing system determines that a higherseeding rate in a particular area of a field is likely to increase theyield of the crop, the agricultural intelligence computing system mayselect the field for performing a seed rate increase trial.

Fields may additionally be identified based on the uniformity,variability and predictability of their yield data. For example, if theagricultural intelligence computing system determines that a field haslow yield variability on short length scale and/or in zones, and higheryield variability on longer length scales and/or between zones, theagricultural intelligence computing system may select the field and/orspecific zones for performing a trial.

In an embodiment, the agricultural intelligence computing systemdetermines a historical risk tolerance associated with a field. Forexample, prior practices for a field may indicate that a field managerhas a higher tolerance for risk-laden activities that may increase theaverage yield for the land. Examples of practices that indicate a higherrisk tolerance include planting fewer hybrids or varieties of seeds,planting hybrids or varieties designed to produce higher yields underoptimal conditions but produce lower yields under non-optimalconditions, a historical tendency to underapply pest-control measurescompared to best management practice, a percentage of the field where anew product is planted for the first time, a number of experiments onthe field, higher seeding population used than averages for asurrounding county or area, widely different seed selection or seedtrait package than typical for a surrounding county or area, relativelyadvanced and/or potentially unproven types of equipment, for examplevariable rate capabilities, application equipment capable of late seasonnitrogen, and/or active downforce management systems on planter, andreferences to riskier activities on social media. Additionally, theagricultural intelligence computing system may receive survey data fromfield manager computing devices indicating a risk tolerance with respectto one or more fields.

Risk tolerance may also be indicated by a field manager opting into oneor more prior trials. For example, if a field manager has agreed toperform a trial during a prior season, the agricultural intelligencecomputing system may identify the field as a good candidate for acurrent trial. Additionally or alternatively, the agriculturalintelligence computing system may store a list of accounts, fields,and/or field managers who have indicated an interest in participating infuture trials. For example, the agricultural intelligence computingsystem may cause display of an interface on a field manager computingdevice that requests an indication as to whether a field manager wouldbe willing to participate in future trials. If the agriculturalintelligence computing system receives a positive indication, theagricultural intelligence computing system may update the list toindicate that the field manager has indicated a willingness toparticipate in future trials.

The agricultural intelligence computing system may be programmed orconfigured to consider these factors individually and/or in combination.For example, the agricultural intelligence computing system may beprogrammed to identify fields with a highest percentage of the fielddedicated to a new product. Additionally or alternatively, theagricultural intelligence computing system may be programmed orconfigured to select fields that include more than a threshold number ofexperiments and are associated with one or more other risky activities.In an embodiment, the agricultural intelligence computing systemcomputes a risk tolerance value. The risk tolerance value may becomputed as a function of any of the above factors. As a simple example,a risk tolerance equation may comprise:

R _(t) =S+N+E _(x) +D+Y+E _(q) +M

where R_(t) is the risk tolerance, S is a value which increases based onthe existence of particular traits in the seeds, N is a value whichincreases with based on the percentage of the field with a new product,E_(x) is a value which increases with a number of identified experimentson the field, D is a value which increases as the difference in seedingpopulation between the field and the average for the county increases, Yis a value which increases based on the predictability of the yieldvariability, E_(q) is a value which increases based on the existence ofparticular types of equipment, and M is a value which increases withreferences to risky activities on social media. These factors may beweighted such that certain factors are considered more heavily thanothers. While the example shown above is additive, other embodiments mayinclude other methods of estimating risk, such as a multiplicative risktolerance equation, such as:

R _(t) =S*N*E _(x) *D*Y*E _(q) *M*R ₀

where R₀ is a base risk rate.

In an embodiment, the agricultural intelligence computing systemdetermines a usefulness of using a field to implement the experimentaltrials. The usefulness of using the field refers to an applicability ofthe trial to one or more other locations. For example, trials may beless useful when performed on a field with unique characteristics suchthat the benefits of the tested action are not applicable to a widerarray of locations. Thus, the agricultural intelligence computing systemmay be programmed to identify fields with characteristics similar toother fields for the purpose of particular trials. For example, for afungicide trial, the agricultural intelligence computing system mayidentify fields with similar ponding conditions, average temperatures,soil moisture, and rainfall as other fields. As another example, a fieldfor a fertilizer trial may be selected based on soil conditions, such aspercent of sand, silt, and clay, being similar to soil conditions ofother fields in the area.

The agricultural intelligence computing system may additionallydetermine usefulness based on data indicating planned practices. Thedata indicating planned practices may be received directly from a fieldmanager computing device and/or inferred from prior practices. Forexample, the agricultural intelligence computing system may store priorplanting data for a field indicating that a particular hybrid of a crophas been planted on a particular field for the last three years. Basedon the stored prior planting data, the agricultural intelligencecomputing system may determine that the particular hybrid has beenplanted on the particular field for the last three years. Theagricultural intelligence computing system may then determine that adifferent hybrid may increase crop yield, cost less, increase cropquality, and/or otherwise benefit the particular field over theparticular hybrid.

Applicability of the trial to one or more other locations may be basedon past events for the field. For example, the agricultural intelligencecomputing system may identify a plurality of fields that had a low yielddue to a particular pest. The agricultural intelligence computing systemmay identify one or more fields of the plurality of fields as candidatesfor the trial based on the one or more fields having suffered anapproximately average loss of yield due to the particular pest.

In an embodiment, the agricultural intelligence computing systemdetermines the operational capabilities associated with a field. Forexample, a field manager computing device may send data to theagricultural intelligence computing system regarding devices on thefield. The data may indicate types of devices, capabilities of devices,and number of devices. If the agricultural intelligence computing systemdetermines that the devices on a field do not match device requirementsfor a trial, the agricultural intelligence computing system may notselect the field. For example, a field manager computing device maydetermine two combines are used on a field of a particular size. If atrial requires a maximum of one combine harvester for a field of theparticular size, the agricultural intelligence computing system may notselect the field as a candidate for participation in the trial.

In an embodiment, the agricultural intelligence computing systemidentifies evidence of existing or previous experiments on a field.Based on the evidence of existing or previous experiments on the field,the agricultural intelligence computing system may select the field as acandidate for performing a trial. The agricultural intelligencecomputing system may identify evidence of experiments based on sectionsof a field that are treated differently from the rest of the field. Forinstance, the agricultural intelligence computing system may identifylocations in the field that have received different seed types, seedingpopulations, and/or product applications such as fertilizer andpesticide. If a determination is made that a field contains one or moreexperiments, the agricultural intelligence computing system may selectthe field as a candidate for participation in the trial.

The above factors may be binary determinations and/or quantitativecomputations. Binary determinations for the above described factors maybe defined by satisfaction of one or more conditions. For example, theagricultural intelligence computing system may determine whether or notthere are current experiments on the field, whether or not the deviceson the field are capable of performing a trial, whether or not thefeatures of a field are within a particular range, whether a modeledbenefit to the field is greater than a threshold value, whether amodeled likelihood of detecting a benefit to the field is greater than athreshold value, and/or whether or not a risk value for a field exceedsa particular threshold value. In response to satisfaction of one or moreconditions, the agricultural intelligence computing system may identifythe field for performance of a trial. For instance, if the onlyrequirement is a risk value over a threshold value, then theagricultural intelligence computing system may select the agriculturalfield if the risk value is above the threshold value. If theagricultural intelligence computing system utilizes two requirements,the agricultural intelligence computing system may select theagricultural field if both requirements are met.

As another example, the agricultural intelligence computing system maycompute a value as a function of a risk tolerance value, a valuedescribing the similarity of the field to other fields, and a valuedescribing the benefit of participating in the trial. The benefit valuemay be computed as a modeled gain in yield and/or profit fromparticipating in the trial. The similarity value may be computed as afunction of differences in one or more attributes of the soil, weather,or other field values between the field and average values for otherfields. The agricultural intelligence computing system may determine ifthe computed value is above a stored threshold value and, in response todetermining that the computed value is above the stored threshold value,select the agricultural field for performing the trial.

While the above examples describe selection of agricultural fields basedon absolutes, such as one or more values exceeding a threshold value, insome embodiments the agricultural fields are selected based on acomparison of values to other agricultural fields. For example, theagricultural intelligence computing system may select one or moreagricultural fields that have the highest benefit values compared to theremainder of agricultural fields for which benefit values were computed.The comparative values may be combined with binary determinations. Forexample, the agricultural intelligence computing system may identify agroup of all agricultural fields with a risk value above a particularthreshold value and select from the group one or more agriculturalfields with the highest benefit values compared to the remainder of theagricultural fields of the group. As another example, the agriculturalintelligence computing system may identify a group of all agriculturalfields with a predicted benefit value above a particular threshold valueand select from the group one or more agricultural fields with thehighest likelihood of detecting a benefit compared to the remainder ofthe agricultural fields of the group.

In some embodiments, a field may be selected for performing a trialbased, at least in part, on a request from a field manager computingdevice. For example, the agricultural intelligence computing system mayprovide a graphical user interface to a field manager computing devicewith options for requesting placement into a trial. In response toreceiving input from the field manager computing device selecting theoption, the agricultural intelligence computing system may utilize themethods described herein to identify one or more trials for an agronomicfield corresponding to an account of the field manager computing device.

5.1. Example Target Identification Implementation

5.1.1. Cross-Grower Field Study

FIG. 13 illustrates a process performed by the agricultural intelligencecomputer system from field targeting to information distribution acrossgrower systems. In some embodiments, the system 130 is programmed toperform automated cross-grower analysis, which can comprisecomputationally targeting grower fields, prescribing experiments togrower fields, collecting data from prescribed experiments, validatingexecution of the prescribed experiments, analyzing the collected data,and distributing analytical results across grower systems.

In step 1302, the system 130 prepares predictive, produce concept basedmodels used to predict yield lifts. In some embodiments, given relevantdata regarding a list of grower fields, the system 130 is programmed todesign specific experiments for specific grower fields. The objective ofan experiment is typically to increase the yield of one or more fieldsby a certain level, although it can also be related to reducing theinputs or an improvement of any other aspect of the fields. The designof an experiment or specifically a targeted trial (to be distinguishedfrom a controlled trial, as further discussed below) includesdetermining which attributes of a field might be related to anexperimental objective and how a change in the values of some of thoseattributes might help achieve the experimental objective. One exampleexperiment is to increase the seeding rate of a field by an amount inorder to increase or lift a crop yield by a certain amount. Anotherexample experiment is to increase the fungicide usage of a field by anamount in order to achieve a reduction in disease spread by a certainamount.

In some embodiments, the system 130 is programmed to manage the list ofgrower fields at a granular level. The system 130 is thereforeconfigured to identify certain boundaries or other problematic areas ofthe fields that will not participate in prescribed experiments, andfurther determine specific strips or squares, with buffer areas inbetween, that will participate in prescribed experiments.

As an example, to determine for which portions of which fields toincrease the seeding rate by a certain amount or by what amount toincrease the seeding rate for specific fields, the system 130 can beconfigured to evaluate, for each field, the hybrid or variety of croptypes, the current seeding rate, the historical yearly yield, how achange in seeding rate affected the yield in the past, how the seedingrate was affected by weather or other variables, or other factorsaffecting the field. While it is called an experiment, the system 130 isconfigured to predict the outcome of the experiment and determinewhether to apply the experiment based on the predicted outcome. Forexample, the system 130 can be configured to apply only thoseexperiments with highest predicted yield lifts in the study. Therefore,each experiment essentially includes a recommendation, such asincreasing the seeding rate by a certain amount, that is to bevalidated.

In some embodiments, targeting grower fields also involves the design ofmultiple experiments to be applied to the fields of one or more growersin a coordinated fashion. For example, a single field can be dividedinto multiple locations for planting multiple hybrids or varieties of acrop. While different fields might specifically benefit from differentexperiments at a certain time, the collection of all the fields canbenefit from coordinated experiments so that as much analytical insightcan be shared across grower fields as possible for long-term benefits.For example, some growers might have a limited number of fields whereonly a limited number of experiments involving a small number ofattributes or a small number of values for a certain attribute can applythis year. Those fields can then benefit from the application ofadditional experiments to other growers' fields that involve differentattributes or different values for the same attributes.

In some embodiments, the system 130 is programmed to start designing,selecting, or applying experiments in response to specific triggers.Such triggers may include when a field is under-performing (e.g., lowcrop biomass or low predicted crop yield within a certain timeframe),when a field is in an unusual condition (e.g., low soil moisture ornitrate), when a change occurs in the environment (e.g., extreme heatwave), or when an experiment prescribed to a similar field has produceda certain outcome. These triggers can be detected from the datacollected during the implementation of the prescribed experiments, asfurther discussed below. Each trigger generally represents anopportunity to improve the performance of a field or gain specificinsight into certain agricultural phenomena or relationships.

In step 1304, the system 130 is programmed to prescribe experiments togrower fields. In some embodiments, the design or selection ofexperiments can be carried out automatically according to apredetermined schedule, such as at the beginning of every year or everygrowing season. The prescribing of experiments can also be performedautomatically. The system 130 can be configured to generate theprescription, plan, or scheme for an experiment that is to be understoodby a human, a machine, or a combination of both. For example, oneexperiment may be to plant certain seeds at certain rates on a certaingrower's fields. The plan for the experiment can include a variety ofdetails, such as the type of seeds, the destination of the seeds withinthe fields, the volume of seeds to plant each day, or the time to plantthe seeds each day.

In some embodiments, the prescription or scheme also includes detailsfor implementing a control trial as opposed to the targeted trial (theoriginal, intended experiment), to enable a grower to better understandthe effect of the targeted trial. Generally, the control trial involvesa contrasting value for the relevant attribute, which could be based onwhat was implemented in the field in the present or in the past. Forexample, when the targeted trial is to increase the seeding rate by afirst amount to increase the yield by a certain level, the control trialmay be to not increase the seeding rate (maintaining the present seedingrate) or to increase by a second amount that is higher or lower than thefirst amount. The prescription can include additional information, suchas when and where the targeted trial and the control trial are to beimplemented on the grower's fields. For example, in one scheme, agrower's field can be divided into locations, and the prescription canindicate that the first location is to be used for the targeted trial,the second location is to be used for the control trial, and thispattern is to repeat three times geographically (the second time on the3^(rd) and fourth locations, and the 3 time on the 5^(th) and the sixthlocations). The prescription can generally incorporate at least somelevel of randomization in managing the targeted trial and the controltrial, such as randomly assigning certain locations to either trial, tominimize any bias that might exist between the two trials.

In some embodiments, the system 130 is programmed to transmit the plandirectly to the agricultural implements of the relevant fields, such asa seed dispenser or another planter registered under the grower of thefields or associated with the specific fields. Depending on how smartthe planter is, the planter may automatically implement at least some ofthe experiment according to the plan or at least display the plan to thegrower as the grower manually operates the planter. For example, theplan can be translated into electronic signals for controlling thewakeup time of the planter, the moving or rotational speed of theplanter, or the route taken by the planter. Alternatively, the system130 can be programmed to transmit the plans or schemes for theexperiment to other smart devices registered under the grower, such as amobile device, to the extent that part of the plan needs to beimplemented manually or simply for informational purposes.

In some embodiments, instead of transmitting the entire scheme for anexperiment to a smart device, whether it is an agricultural implement ora person digital assistant, the system 130 is programmed to transmit thescheme incrementally and timely. For example, when the scheme involvesthe performance of daily tasks, the system 130 can be configured to senda portion of the scheme corresponding to each day's work every day. Thesystem 130 can also be configured to deliver reminders to the grower'smobile devices, for example, for the performance of certain tasksaccording to the scheme.

In step 1306, the system 130 is programmed to collect data fromprescribed experiments. In some embodiments, the system 130 isprogrammed to receive data from the same agricultural implements towhich the experiment schemes or plans were transmitted, or from the samefield manager computing device, including mobile devices, registeredunder the growers. The agricultural implements can be equipped withsensors that can capture many types of data. In addition to data relatedto the variables involved in the experiment, such as the volume of seedsactually planted, the time of actual planting, the actual moving orrotational speed of the agricultural implement, the route actually takenby the agricultural implement, or the crop yield actually achieved, anagricultural implement can capture additional data related to theweather, such as the amount of sunlight, humidity, pollen, wind, etc.The agricultural implement can also record additional data related toits internal state, including whether different components arefunctioning properly, when the agricultural implement is cleaned ormaintained, how often the agricultural implement is used, or whether theagricultural implement is used in any unusual manner. Some of thesetypes data can be observed by sensors integrated with personal computingdevices or directly by growers and subsequently reported via thepersonal computing devices to the system 130. In general, the data canbe transmitted by an agricultural implement or a personal computingdevice to the system 130 once the data becomes available, upon requestby the system 130, or according to a predetermined schedule.

In step 1308, the system 130 is programmed to validate execution of theprescribed experiments. In some embodiments, the system 130 isprogrammed to determine whether the prescribed experiment is properlycarried out according to the plan or scheme for the experiment. Theobjective is to enable proper implementation of the prescribedexperiments in order to achieve the predicted results. For the variablesinvolved in the scheme, the system 130 is programmed to compare theactual value, such as the volume of seeds actually planted at a specificlocation within a particular period of time, such as one hour, and theprescribed value. The system 130 is configured to report any detecteddiscrepancy. For example, at least a warning can be sent to the grower'spersonal computing device that if the plan is not strictly followed, theexpected benefit of the prescribed experiment will not be achieved. Awarning may appear in any form known in the art such as a pop-up,instant message, e-mail or other text message. The warning couldalternatively be presented as a static or moving or flashing visual orgraphic such as a color coded visual such as a green light indicatingthat the experiment is in compliance or red light showingnon-compliance. Compliance (or non-compliance) could also be based onwhether a value falls within a predetermined tolerance or range. Forexample, the agricultural intelligence computer system may determinewhether a compliance level is below a threshold value. For instance, ifthe compliance level relates to a percentage of a location that is incompliance, the system may determine whether the percentage of thelocation in compliance is below 90%.

In some embodiments, the system 130 is programmed to evaluate othercollected data and recommend remedial steps. Specifically, the system130 can be configured to transmit a series of steps for diagnosingwhether a component of the agricultural implement is functioningproperly. For example, when the volume of seeds actually planted at aspecific location within a one-hour span is greater than the prescribedvalue, the bin holding the seeds to be planted or the scale for weighingthe seeds to be planted may be out of order. Therefore, the system 130might be programmed to request an inspection of the bin or the scale.When the malfunctioning of the agricultural implement is detecteddirectly by sensors or through certain diagnosis, the system 130 can beprogrammed to transmit a similar recommendation for recalibrating orrepairing the agricultural implement. On the other hand, upon adetermination that certain steps are completely skipped, the system 130can be programmed to transmit an instruction to follow those steps, or asuggestion for readjusting reminder alarms or for inspecting theagricultural implements.

In some embodiments, the system 130 can be programmed to validate theexecution of each prescribed experiment according to a predeterminedschedule, such as every month, or as soon as error signals orapplication data are received. The system 130 can also be programmed tovalidate the execution of all prescribed experiments according to aspecific paradigm, such as one based on randomly sampling, in order toconserve resources.

In step 1310, the system 130 is programmed to analyze the collecteddata. In some embodiments, the system 130 is programmed to furtheranalyze the data, to adjust the predictions or the plans for theprescribed experiments, or to glean specific insight that can be used indesigning future experiments. Such analysis can be performedperiodically, at the end of a season or a year, or upon request by agrower.

In some embodiments, when a prescribed experiment was not properlycarried out, the predicted result might not be obtained, and the system130 can be programmed to adjust the prediction based on how the plan forthe prescribed experiment was followed. For example, the system 130 canbe configured to consider that the actual seeding rate was only 80% ofthe prescribed seeding rate overall, due to erroneous calibration of theagricultural implement, the skipping of certain planting steps, or otherreasons, in determining the predicted crop yield might be only 80% of orotherwise less than the predicted or recommended crop yield. The system130 can also be programmed to generate a series of remedial steps inorder to realize the original prediction. For example, when the actualseeding rate was only 80% of the prescribed seeding rate overall, thesystem 130 can be configured to compensate for it by prescribing aseeding rate that was 20% or otherwise higher than originally prescribedfor the rest of the experiment.

In some embodiments, the system 130 can be programmed to determine whyeven when the prescribed experiment was properly carried out, thepredicted outcome was not achieved. The comparison of the datarespectively gathered from the targeted trial and the control trial canoften be used to eliminate certain factors from consideration. Thesystem 130 can also be configured to detect correlations between theobjective of the experiment and other field attributes or externalvariables. The system 130 can also be configured to detect patterns fromthe outcomes of similar experiments, which can help identify outliersand point to field-specific issues. The reasons behind the discrepanciesbetween the predicted outcomes and the actual outcomes can be used fordesigning future experiments or generating predictions for futureexperiments. For example, upon detecting a significant correlationbetween the crop type and the seeding rate with respect to the cropyield, the system 130 can be configured to target specific fields inwhich certain types of crops are typically grown for an experiment thatrelates a seeding rate to the crop yield. Similarly, the system 130 canbe programmed to predict different levels of crop yield depending on thetypes of crops grown in the specific field.

In some embodiments, the system 130 is programmed to design incrementalexperiments. To test a relatively new hypothesis, the system 130 can beconfigured to prescribe conservative experiments by introducing arelatively small change to one of the attributes or variables. When theactual outcome of the last prescribed experiment agrees with thepredicted outcome, the system 130 can be programmed to then introducefurther change to the attribute or variable. In other embodiments, thesystem 130 is programmed to consider the outcomes of two prescribedexperiments that were applied to two similar fields and determinewhether combining the two experiments might be permissible andbeneficial. For example, when the relationship between the seeding rateand the yield and between the soil moisture and the yield have beenclearly and separately demonstrated in two similar fields, a futureexperiment might be to increase the seeding rate and the soil moisturein the same experiment applied to the same field.

In step 1312, the system 130 is optionally programmed to distributeanalytic insights across grower systems. In some embodiments, the system130 is programmed to present summaries, tips, or further recommendationsgenerated from analyzing the data obtained from the multitude ofprescribed experiments across grower fields. The system 130 can beconfigured to transmit a report to each grower system, such as thegrower's mobile device, that shows aggregate statistics over all theprescribed experiments or certain groups of prescribed experiments. Thereport can also indicate how the grower's fields have performed comparedto the other growers' fields and indicate possible reasons based on ananalysis of the difference in performance between the grower's fieldsand the other growers' fields. The report can highlight other prescribedexperiments that are similar to the ones prescribed to the grower'sfields. The report can also outline possible experiments to apply to thegrower's fields in the future and solicit feedback from the grower.

In some embodiments, some or all of these steps 1302 through 1312 can beexecuted repeatedly, iteratively, or out of order. For example, datacapturing and execution validation can take place periodically during aseason.

5.1.2 Field Targeting

In some embodiments, the system 130 is programmed to build a modelcomprising computer-executable instructions for predicting product (cropyield) responsiveness of a field to a change in seeding rate. The system130 is programmed to initially establish certain baselines fromhistorical data that spans a number of years of a number of fieldsacross different growers associated with different grower devices. Thehistorical data can be obtained from internal trials and experiments orfrom external data sources. The number of fields can have common valuesin certain characteristics, such as the crop hybrid grown in a field,the location of a field, or the yield lift management practice for afield, as further discussed below. An average relationship between thecrop density and the crop yield for a given hybrid can be computed fromthe historical data to provide a benchmark. Such a relationship istypically reflected in a quadratic curve. FIG. 14 illustrates an examplerelationship between the crop density and the crop yield for a givenhybrid. The X-axis 1402 corresponds to the crop density or seeding ratein plants per acre (ppa), and the Y-axis 1404 corresponds to the cropyield in bushels per acre. In this example, the seeding rate data andthe corresponding crop yield data is fitted into a quadratic curve 1408.The shape and size of the quadratic curve 1408 can be characterized bythe slope line 1410 from the data point 1412 corresponding to the lowestseeding rate to the data point 1406 corresponding to the optimal seedingrate and the highest crop yield. The system 130 can be programmed toselect a threshold for product responsiveness based on the averagerelationship between the crop density and the crop yield. For example,as the slope of the slope line 1410 here is about 2.8, the threshold canbe set to 1.5, so that a field producing a 1.5 bushel yield lift forevery 1,000 seed increase would be considered responsive, as furtherdiscussed below.

In some embodiments, instead of focusing on reaching the optimal seedingrate, the system 130 is programmed to allow flexibility in seeding rateincrease. Specifically, instead of focusing on the relationship betweenthe current seeding rate and the optimal seeding rate, the system 130 isconfigured to consider other factors, such as a target seeding rate lessthan the optimal seeding rate or a crop yield lift corresponding to achange to the target seeding rate. For example, the system 130 can beconfigured to cluster certain fields by hybrid and by location, andcompute the average seeding rate within a cluster as the target seedingrate. The same threshold determined from the slope line noted abovecould still apply in evaluating product responsiveness with respect tothe target seeding rate.

In some embodiments, the system 130 is configured to adopt a morecomplex approach, such as building a decision tree that classifies givenfields with seeding rate data and crop yield data into different classescorresponding to different crop yield lift amounts based on the initial(current) seeding rate, the target seeding rate, the difference betweenthe initial seeding rate and the target seeding rate, or otherattributes related to the fields. Examples of the other attributes couldrange from inherent attributes, such as soil moisture level, toenvironmental attributes, such as soil management practice. Othermachine-learning methods known to someone skilled in the art forcapturing various relationships between the seeding rate (in conjunctionwith other attributes) and the crop yield lift, such as neural networksor regression techniques, can also be used. The more complex approachcan produce more granular information beyond whether a lift is possibleand towards how much lift might be possible.

In some embodiments, the system 130 is programmed to next determinegrower-specific product responsiveness. For a grower's field, the system130 is programmed to similarly review the historical crop yield dataover a number of years for a specific zone within the field or the fieldon average and identify the hybrid and current seeding rate for thefield or zone. Referring back to FIG. 14 illustrating the relationshipbetween the crop density and the crop yield for an appropriate hybrid,the slope threshold discussed above, such as 1.5 based on the slope forthe first slope line 1410, can be used to determine whether the grower'sfield is likely to be responsive to a certain seeding rate increase. Forexample, a second slope line 1414 can be formed from the data point 1416corresponding to the current seeding rate and the data point 1406corresponding to the optimal seeding rate and the highest crop yield.When the current seeding rate is smaller than the optimal seeding rate,the slope of the second slope line will be positive but could be aboveor below the threshold noted above. The system 130 can be configured todeem the field responsive to a seeding rate increase to the optimalseeding rate when the slope of the second slope line is at or above thethreshold. When the current seeding rate is larger than the optimalseeding rate, the slope of the second slope line will be negative. Thesystem 130 can then be configured to evaluate the product responsivenessof the field to a seeding rate decrease. The system 130 can beconfigured to similarly evaluate the product responsiveness of the fieldto a seeding rate increase to a target seeding rate less than theoptimal seeding rate.

In some embodiments, the system 130 is programmed to apply one of themore complex approaches, such as the decision tree discussed above, toevaluate grower-specific product responsiveness. At least the currentseeding rate of a grower's field and an intended or target seeding ratefor the grower's field could be fed into the decision tree, and a rangeof crop yield lift values can be estimated by the decision tree, whichcan be further categorized into responsive or unresponsive or othergranular or different classes.

In some embodiments, the system 130 is programmed to evaluate thegrower's field management practice in terms of lifting crop yield overtime. FIG. 15 illustrates example types of management practice. TheX-axis 1502 corresponds to the year, the Y-axis 1504 corresponds to thetarget or actual crop yield. The type of management practice in termslifting crop yield can be reflected in various curves. The curve 1506indicates an aggressive type, where there is steady and significantincrease in crop yield one year after another. The curve 1508 indicatesa conservative or pragmatic type, where there is no significant increasein crop yield from one year to the next. The curve 1510 indicates anunrealistic type, where there is no change in crop yield for some yearsbut then there is a sharp increase. Identifying the type of managementpractice or other aspects external to the soil can be helpful inprescribing actual experiments to targeted growers' fields. In otherembodiments, the type of management practice can also be an inputattribute for a machine learning method discussed above.

In some embodiments, the system 130 is programmed to also evaluate thedegree of variability within the grower's field. Actual density datamight be available for different zones within the field, or aerialimages of the field can be analyzed via image analysis techniques knownto someone skilled in the art. Based on such data, the system 130 can beprogrammed to determine whether the crop densities or seeding rates aremore or less constant across the field or vary substantially amongdifferent zones. Such determination can also be useful in prescribingactual experiments to targeted growers' fields.

In some embodiments, the system 130 is programmed to target thosegrowers' fields that are responsive to increasing seeding rates anddesign experiments for those fields. Each design can have variousparameters, such as the crop hybrid, the zone variability, or theseeding rate increase. FIG. 16 illustrates an example process performedby the agricultural intelligence computer system to determine the crophybrid for a grower's field or the zones thereof. In some embodiments,in step 1602, the system 130 is programmed to communicate with a growerdevice associated with a targeted field. Specifically, the system 130 isconfigured to receive an intended density or seeding rate for the fieldfrom the grower device. The intended density is typically larger thanthe current aggregate density in the field. The system 130 is programmedto then determine how the intended density compares with a targetdensity for the field. The target density may be predetermined for thefield based on a combination of approaches, such as a comparison with acomputed average or optimal seeding rate, a classification via anestablished seeding-rate decision tree, or an evaluation of the type ofmanagement practice in terms of lifting crop yield, as discussed above.The target density is also typically larger than the current aggregatedensity in the field. When the intended density is below the targetdensity, in step 1604, the system 130 is configured to then receive adecision regarding whether to increase the intended density to thetarget density from the grower device. When the decision is not toincrease the intended density, in step 1606, the system 130 isconfigured to compute the difference of the intended density from thetarget density. When the difference is above a certain threshold so thatthe intended density remains sufficiently low, the system 130 isconfigured to recommend a flex or semi-flex hybrid for the field. Forexample, the certain threshold can be 80% of the target density. In someembodiments, when the intended density is at or above the target densityreaching a substantially large value, in step 1608, the system 130 isconfigured to recommend a fixed or semi-flex hybrid for the field.

In some embodiments, the system 130 is programmed to next respond tozone variability within the targeted field. Specifically, in step 1610,the system 130 is configured to determine whether there is significantvariability in seeding rates among different zones within the field andwhether the current aggregate density considered so far is merely anaggregate across the field. The system 130 may be configured to furtherdetermine whether a certain zone may benefit from higher seeding ratesfrom the intended seeding rate, based on the difference between thecurrent seeding rate of the certain zone with respect to the currentaggregate density, the intended seeding rate, and the target seedingrate. For example, when the difference between the current seeding rateof the certain zone and the current aggregate density is above aspecific threshold, such as 30% of the current aggregate density, andwhen the intended density is less than the target density, the currentseeding rate of the certain zone may be increased to be beyond theintended density. In such cases where a yield opportunity exists for aseeding rate that is higher than the intended seeding rate, in step1612, the system 130 is configured to recommend a fixed or a semi-flexhybrid due to the relatively large density limitation. In other caseswhere no yield opportunity exists for a seeding rate that is higher thanthe intended seeding rate, in step 1614, the system 130 is configured torecommend no hybrid change for the static-rate field. In addition, thesystem 130 may be configured to further determine whether a certain zonemay benefit from lower seeding rates from the intended seeding rate.Such a zone may be a risk zone suffering from drought or other naturalor environmental attack. Therefore, in step 1616, the system 130 may beconfigured to recommend a flex hybrid for such a zone corresponding to arelatively low current seeding rate or intended seeding rate tofacilitate retainment of water or encourage further crop growth.

FIG. 17 illustrates an example process performed by the agriculturalintelligence computer system of targeting grower fields for crop yieldlift.

In some embodiments, in step 1702, the system 130 is programmed toreceive crop seeding rate data and corresponding crop yield data over aperiod of time regarding a group of fields associated with a pluralityof grower devices. Such data is used to establish benchmarks fordetermining product responsiveness to a seeding rate increase for agrower's field. The group of fields may be selected from those fieldsthat share values with the grower's field in certain characteristics,such as the crop hybrid grown in a field, the predicted yield lift for achange in management practice for a field, or the location of a field.The time coverage of the data allows the effect of seeding rateincreases on the crop yield lift to be revealed. As discussed above, atleast an optimal seeding rate and a corresponding threshold on theeffect of a seeding rate increase on the crop yield lift can bedetermined, and more complex approaches can be developed forcharacterizing or determining the potential impact of a seeding ratechange on the crop yield in a grower's field and ultimately whether thegrower's field should be targeted for specific experiments to lift thecrop yield. In step 1704, the system 130 is programmed to receive acurrent seeding rate for a grower's field associated with one of aplurality of grower devices. The current seeding rate can be anaggregate across different zones within the field.

In step 1706, the system 130 is programmed to further determine whetherthe grower's field will be responsive to increasing a crop seeding ratefor the grower's field from the current seeding rate to a target seedingrate based on the crop seeding rate data and the corresponding cropyield data. The target seeding rate can be set as the optimal seedingrate or a value that is more consistent with the yield lift managementpractice for the field or other intent of the grower. Essentially, fromthe relationship between the seeding rate and the crop yielddemonstrated by the group of fields, which can be derived from the cropseeding rate data and the corresponding crop yield data, the system 130is configured or programmed to estimate an impact of a seeding ratechange from the current seeding rate to the target seeding rate in thegrower's field and in turn determine whether the grower's field willeffectively respond to the seeding rate change by producing the desiredcrop yield lift.

In step 1708, in response to determining that the grower's field will beresponsive, the system 130 is programmed to target the grower's fieldfor an experiment to increase the crop yield and prepare a prescriptionfor the experiment, including a new crop seeding rate and a specificcrop hybrid to be implemented in the grower's field. The new cropseeding rate can be the target seeding rate unless it is overridden byan intended seeding rate provided by the grower device. Any recommendedchange in the crop hybrid is generally consistent with the change in theseeding rate, and it can be implemented incrementally within the fieldor gradually over time to be able to achieve as much of the estimatedcrop yield lift as possible. Furthermore, the system 130 can beconfigured to evaluate the variability in crop yield within the grower'sfield and prepare a more granular prescription. Such evaluation can bebased on physical samples from the field or aerial images of the field.A higher seeding rate than the new seeding rate can often beadditionally prescribed to a zone having a seeding rate higher than thecurrent seeding rate. Similarly, a lower seeding rate than the newseeding rate can be additionally prescribed to a zone having a seedingrate lower than the current seeding rate.

As illustrated in FIG. 13, the system 130 can be programmed to furthercollect results of implementing the prescribed experiments from the onegrower device or directly from agricultural implements the prescribedthe experiments. Specifically, the predicted crop yield lift can bevalidated against the actual crop yield lift. The system 130 can beconfigured to then distribute data related to the experiment and thevalidated results to the other grower devices associated with the groupof fields. The seeding rate data and the crop yield data can also beupdated with the validated result to enable more accurate modeling ofthe relationship between crop seeding rates and crop yield.

6. Trial Design

In an embodiment, the agricultural intelligence computing systemdetermines where to place testing locations based on one or moremanagement zones. Management zones refer to regions within anagricultural field or a plurality of agricultural fields that areexpected to have similar limiting factors influencing harvested yieldsof crops. While management zones are generally described with respect toportions of a single field, management zones may be designed toencompass locations in a plurality of fields spanning a plurality ofgrowers. Methods for identifying management zones are described furtherin U.S. Patent Pub. 2018-0046735A1. The agricultural intelligencecomputing system may identify benefits of using a new product, differentseeds, and/or management practices for a management zone. Theagricultural intelligence computing system may identify testinglocations within the management zone so that effects of performing thetrial can be compared to the rest of the management zone.

In an embodiment, the agricultural intelligence computing systemidentifies management zones based on a type of trial being performed.For example, two locations on a field may comprise different soil types,but have a similar yield and a similar pest problem. For purposes ofimplementing a pesticide trial, the two locations may be treated as asingle management zone. In contrast, for purposes of implementing afertilizer trial which is dependent on the soil type, the two locationsmay be treated as different zones.

In an embodiment, management zones are identified based on bothresponsiveness and total yield. The agricultural intelligence computingsystem may determine the responsiveness of areas in a field toapplications of products and/or different management practices based onprior yield data, soil data, imagery, other crop data, and managementpractices. For example, the agricultural intelligence computing systemmay identify two equivalent sites where fertilizer was applied on oneand not applied on the other. Based on differences in the yield betweenthe two fertilizer rates on equivalent sites, the agriculturalintelligence computing system may determine a responsiveness tofertilizer for those and other equivalent locations on the field.

The responsiveness may be a computed value and/or a binarydetermination. For example, the agricultural intelligence computingsystem may determine that a location with more than a threshold absoluteor percentage change in yield is considered to have high responsivenesswhile areas with less than the threshold absolute or percentage changein yield is considered to have low responsiveness. The agriculturalintelligence computing system may generate zones which have similarresponsiveness and similar yields. For example, the agriculturalintelligence computing system may generate zones that have highresponsiveness and high yield and separate zones that have highresponsiveness and low yield, based on yield data, plant data, soildata, weather data, and/or management practice data. Thus, theagricultural intelligence computing system may generate both highresponsive zones and low responsive zones that are constrained by totalyield.

Within the zones, the agricultural intelligence computing system mayidentify possible locations for testing locations. The size and shape oftesting locations may be determined based on variability in a particularfield or zone. Variability, as used herein, refers to the amount thetotal yield tends to vary within a field and/or management zone. Theamount of variance may include both magnitude of variance and a spatialcomponent of the variance. For example, if the yield fluctuates rapidlywithin a small region of a management zone, the agriculturalintelligence computing system may determine that a larger testinglocation should be implemented. In contrast, if the yield has longlength scale trends in yield, a smaller testing location may beimplemented. The optimal size, shape, and number of testing locationscan be determined directly from historic yield variability data. In oneembodiment, the historic yield data is broken into uniform grids ofpotential testing locations different sizes; the total testing arearequired, including buffer areas around testing locations, is calculatedfor each testing location size given an acceptable statisticalsignificance for the answer; and the optimal configuration is the onethat minimizes the total testing area. The optimal size, shape, andnumber of testing locations can also be determined from modeled yieldvariability data from historic images, or modeled yield variability databased on predictors to a model trained on historic yield variabilitydata. Further, based on the size of testing location, the agriculturalintelligence computing system may identify a shape of the testinglocation in order to maximize a number of testing locations that can befit into a single zone. For example, if a zone is particularly narrow,the agricultural intelligence computing system may select a narrowrectangle as the shape of the testing location.

Using the identified size and shape of the testing locations, theagricultural intelligence computing system may determine a plurality ofpossible locations for placing the testing locations in the field. Theagricultural intelligence computing system may then select a subset ofthe plurality of possible locations for placing the testing locations.In an embodiment, the agricultural intelligence computing systemdetermines a number of testing locations to implement based on trialrequirements and/or user selection. For example, a constraint of a trialmay be that at least two testing locations are planted in eachmanagement zone. As another example, a field manager may indicate,through a graphical user interface executing on the field managercomputing device, that the field manager is willing to dedicate fivepercent of the field to the trial. The agricultural intelligencecomputing system may thus compute the number of testing locations as:

$N = \frac{A_{f}*D}{A_{T}}$

where N is the number of testing locations, A_(f) is the area of thefield, D is the percentage of the field dedicated to trials, and A_(T)is the area of the testing locations. As another example, a fieldmanager may indicate, through a graphical user interface executing onthe field manager computing device, that the field manager wants todetect a minimum treatment effect of a certain number of bushels peracre with a given signal to noise ratio. The agricultural intelligencecomputing system may thus compute the number of testing locations as:

$N = \left( \frac{SNR*\sigma}{T} \right)^{2}$

where N is the number of testing locations, SNR is the signal to noiseratio, a is the standard deviation of the average yield differencebetween potential testing locations, and is the desired minimumdetectable treatment effect.

In an embodiment, the agricultural intelligence computing systemrandomly selects locations of the plurality of potential locations untilthe determined number of testing locations have been identified. Theagricultural intelligence computing system may constrain the randomselection by selecting at least two locations for a zone where a firstlocation is selected, thereby allowing for both a test group and acontrol group. The agricultural intelligence computing system may alsoconstrain the random selection to ensure that testing locations areplaced in a maximum number of zones. Additionally or alternatively, theagricultural intelligence computing system may present, through agraphical user interface on the field manager computing device, aplurality of possible locations for testing locations. The field managermay select particular locations of the plurality of possible locationsand send the selections to the agricultural intelligence computingsystem.

In an embodiment, the agricultural intelligence computing system selectslocations for the testing locations in order to minimize the effect ontotal yield from performing the trial. For example, the agriculturalintelligence computing system may prioritize areas of the field thathave had historically lower yields, thereby reducing any possiblenegative impacts on the yield of the field. Additionally oralternatively, the agricultural intelligence computing system mayprioritize location for the testing locations in a manner that maximizesbenefit of performing the trials. For example, for a pesticide trial theagricultural intelligence computing system may select regions of thefield that have historically received the highest negative impact onyield due to pests.

The prioritizations based on minimizing the effect on yield ormaximizing the benefits of performing the trials may be implementedalong with other constraints. For example, the agricultural intelligencecomputing system may initially attempt to place at least two testinglocations in each management zone. The agricultural intelligencecomputing system may then pseudo-randomly select additional testinglocations while assigning higher weights to locations with low yields orhigh responsiveness. As another example, the agricultural intelligencecomputing system may attempt to place testing locations in a minimum ofa high responsiveness and high yield location, a high responsiveness andlow yield location, a low responsiveness and high yield location, and alow responsiveness and low yield location.

FIG. 8 depicts an example of implementing testing locations on a field.The field of FIG. 8 is broken up into different management zones, eachmarked by a color. Dark brown polygons depict possible testinglocations. In embodiments, they are placed to span management zones. Inembodiments, adjacent polygons with the same management may be merged.In embodiments, of the possible testing locations, the agriculturalintelligence computing system randomly selects locations to implementthe testing locations. In embodiments, the agricultural intelligencecomputing system selects locations according to one or more constraints.For example, in FIG. 8, a possible constraint is a minimum locationwidth of 120 feet to be compatible with field manager equipment. Anotheris that at least 40 testing locations are implemented in this field toachieve a predicted minimum significant detectable treatment effect.Additionally, in FIG. 8 the testing locations are implemented such thateach has an unmarked control location randomly assigned to theequivalently sized area on one or the other of its two long sides.

7. Target Identification and Trial Design Based on Short Length FieldVariability

In an embodiment, the agricultural intelligence computer system computesa short length field variability for purposes of performing a trial onan agricultural field. The short length field variability indicates theextent to which a field varies across small distances. FIG. 20 depicts amethod for modeling short length variability within a field.

At step 2002, a map of an agricultural field is received. For example,the agricultural intelligence computer system may receive aerial imageryof an agricultural field. Additionally or alternatively, theagricultural intelligence computer system may receive input delineatingboundaries of an agricultural field, such as through a map displayed ona client computing device and/or input specifying latitude and longitudeof field boundaries. The map may also be generated from one or moreagricultural implements on the agricultural field. For example, aplanter may generate as-applied data indicating a seeding type and/orseeding population along with geographic coordinates that correspond tothe seeding type and/or seeding population. The planter may send theas-applied data to the agricultural intelligence computer system.

In an embodiment, the system additionally receives agricultural yielddata for the agricultural field. For example, an agricultural implement,such as a harvester, may generate data indicating a yield of a portionof the agricultural field and send the yield data to the agriculturalintelligence computer system. The agricultural intelligence computersystem may generate a yield map indicating, for each location on theagricultural field, an agricultural yield.

At step 2004, a grid overlay is generated for the map of theagricultural field. For example, the agricultural intelligence computersystem may generate a grid with a plurality of cells to overlay on themap of the agricultural field. Generating the grid may compriseidentifying a field boundary, determining a width and length for thegrid cells, generating a first set of parallel lines separated by adistance equal to the width of the grid cells and generating a secondset of parallel lines that are perpendicular to the first set ofparallel lines and are separated by a distance equal to the width of thegrid cells. The width of the grid cells may be determined based on thewidth of a head of a combine, the width of application equipment, thewidth of management equipment, or the width of a planter for theagricultural field. For example, a multiple of an equipment width can beused. Specifically, if the combine head is 30 ft wide, the width of thegrid cells may be a multiple, 30 ft, 60 ft, 90 ft, 120 ft, and so on.

For another example, a common multiple can be used. Specifically, if thecombine is 20 ft wide and the planter is 40 ft wide and the differentmanagement practices are planting related, like two seeding populationdensities, the width of the grid cells maybe a common multiple of bothwidths, 40 ft, 80 ft, 120 ft, and so on. The width of the grid cells mayalso be increased to allow for getting yield data from each treatmenteven if the combine is misaligned with the other management equipment.For example, if the combine is 20 ft wide and the fungicide applicationequipment is 30 ft wide and the different management practices areapplying fungicide or not, the width of the grid cells may be 60 ft, 90ft, 120 ft, and so on, with the combine able to harvest one or morepasses entirely within each treatment even if the combine is not alignedwith the fungicide application equipment. The width of the grid cellsmay also include a buffer to allow for local mixing between managementpractices. For example if the combine is 20 ft wide and the fungicideapplication equipment is 60 ft wide and the different managementpractices are applying fungicide or not, the width of the grid cells maybe 60 ft, 90 ft, 120 ft, and so on, with the combine able to harvest oneor more passes entirely within one treatment even if 20 ft on each sideof each treatment boundary is thrown out as a buffer area to allow forany drift in the fungicide. The length of the grid cells may bedetermined using the methods described herein. As an example, each gridcell may be 120 ft×300 ft.

FIG. 21 depicts an example of a grid overlay on a map used for computingshort length yield variability. Map 2102 comprises a grid overlaying amap of an agricultural field. As shown in map 2102, the first verticalline is generated at a grid cell width away from the leftmost boundaryof the map whereas the first horizontal line is generated at a grid celllength away from the bottommost boundary of the map. In an embodiment,the agricultural field additionally includes management zones. Forexample, map 2104 depicts a grid overlay on a map of an agriculturalfield which contains three management zones that are differentiated bycolor. The management zones refer to sections of the agricultural fieldwhich receive similar management treatment or have previously beengrouped based on shared characteristics.

Referring again to FIG. 20, at step 2006, a plurality of adjacent gridcells is selected. For example, the agricultural intelligence computersystem may randomly or pseudo-randomly select, from the grid cells ofthe grid overlay, a first grid cell. The agricultural intelligencecomputer system may then randomly or pseudo-random select, from adjacentgrid cells of the first grid cell, a second grid cell. Additionally oralternatively, the agricultural intelligence computer system may utilizea specific rule for selecting the adjacent cell, such as initiallyattempting to select a cell from the right of the first cell followed bythe cell to the left of the first cell and so on. If there are noadjacent grid cells to the first grid cell, the agriculturalintelligence computer system may discard the selected first grid celland randomly or pseudo-randomly select a different grid cell.Additionally, the agricultural intelligence computer system may randomlyor pseudo-randomly select sets of adjacent cells, one for each differentmanagement practice.

In an embodiment, the agricultural intelligence computer systemidentifies complete grid cells from which to select the first grid celland/or the second grid cell. For example, map 2102 in FIG. 21 includesincomplete grid cells, such as the cells abutting the boundary of theagricultural field. The agricultural intelligence computer system mayremove the incomplete grid cells and select the first grid cell andsecond grid cell from the remaining grid cells. For the purpose ofselection, the agricultural intelligence computer system may treat theincomplete grid cells as non-existent.

In an embodiment, the agricultural intelligence computer system alsoidentifies grid cells that are completely in a single management zonefrom which to select the first grid cell and/or the second grid cell.For example, map 2104 includes grid cells that comprise multiplemanagement zones due to the border for the management zones runningthrough the grid cell. The agricultural intelligence computer system mayremove grid cells that comprise multiple management zones and select thefirst grid cell and second grid cell from the remaining grid cells. Forthe purpose of selection, the agricultural intelligence computer systemmay treat the grid cells comprising multiple management zones asnon-existent.

In an embodiment, adjacent cells are selected to be in the samemanagement zone. Map 2106 in FIG. 21 depicts a selection of a pluralityof sets of adjacent cells. Each set of adjacent cells in map 2106comprises two cells in the same management zone, even though the sets ofadjacent cells span management zones.

At step 2008, for each set of adjacent grid cells, a difference inaverage yield between the adjacent cells is computed. For example, theagricultural intelligence computer system may store data identifying theaverage yield for each grid. The data identifying the average yield maybe based on harvesting data indicating yield for a portion of theagricultural field covered by the cell and/or modeled based on receiveddata or imagery. The agricultural intelligence computer system maycompute an absolute value of the difference between adjacent cells ineach set. Thus, if one cell has an average yield of 170.8 bushels peracre and the adjacent cell has an average yield of 171.2 bushels peracre, the system may compute the difference in average yield between theadjacent cells as 0.4 bushels per acre.

At step 2010, a short length variability for the agricultural field isdetermined based, at least in part, on the difference in average yieldfor each set of adjacent cells. For example, the agriculturalintelligence computer system may identify a median of the differencesacross the plurality of sets of adjacent cells and select the medianvalue as the short length variability for the agricultural field.

At step 2012, based on the short length variability, one or morelocations are selected for performing trials. Methods for selectingfields and/or locations on fields for performing trials are describedfurther herein.

At step 2014, the system generates a prescription map comprising one ormore different management practices in the selected locations. Forexample, the system may begin implementation of the trial by generatinga prescription map where the selected locations include a differentplanting population, nutrient application, chemical application,irrigation, and/or other management practice that is different than oneor more surrounding locations. Methods of generating a prescription mapare described in Section 7.6.

7.1. Modeling Variability

In an embodiment, short length variability is modeled based on aplurality of factors. For example, the system may model the averageyield for each cell as a function of one or more of elevation, organicmatter, nutrient levels, soil type or property, and/or other field levelvariables. Additionally or alternatively, the system may model thevariability between adjacent cells as a function of a plurality offactors. Each function, equation and calculation described in thissection may be programmed as part of the instructions that have beendescribed for FIG. 1 to receive data values for the specified parametersand to calculate by computer the transformations that are shownmathematically to yield the results that are described.

As an example, the system may model short length variability accordingto the following function:

$V = {\sum\limits_{i = 1}^{n}\frac{{{w_{A}\left( {A_{i,a} - A_{i,b}} \right)} + {w_{B}\left( {B_{i,a} - B_{i,b}} \right)} + {\ldots \; {w_{N}\left( {N_{i,a} - N_{i,b}} \right)}}}\;}{n}}$

where N_(i,a)−N_(i,b) is the difference in the Nth attribute betweencell a and cell b of the i-th set of adjacent pairs and w_(N) is aweight for the Nth attribute. For example, if the short lengthvariability was modeled based on elevation, pH value, and organicmatter, the short length variability equation would take the form of:

$V = {\sum\limits_{i = 1}^{n}\frac{{{w_{E}\left( {E_{i,a} - E_{i,b}} \right)} + {w_{pH}\left( {{pH}_{i,a} - {pH}_{i,b}} \right)} + {\ldots \; {w_{O}\left( {O_{i,a} - O_{i,b}} \right)}}}\;}{n}}$

where E is the average elevation, pH is the average pH value, and O isthe average organic matter for each grid cell.

While the above equation computes short length variability for the fieldas an average of variabilities at individual locations, in an embodimentdifference value is computed for each location according to:

D _(i) =w _(A)(A _(i,a) −A _(i,b))+w _(B)(B _(i,a) −B _(i,b))+ . . . w_(N)(N _(i,a) −N _(i,b))

and the short length variability is determined as the median differencevalue amongst the plurality of locations.

In an embodiment, the weights for the above equations are empiricallychosen. Additionally or alternatively, the agricultural intelligencecomputer system may compute the weights based on yield variation datafrom other fields. For example, agricultural intelligence computersystem may receive, for a plurality of pair of adjacent locations, dataidentifying the yield for each location of the pair and data identifyinga plurality of attribute values for each location and pair. The systemmay then compute weights for the above equation by selecting weightsthat minimize the following equation:

${\sum\limits_{i}^{n}Y_{i,a}} - Y_{i,b} - \left( {{w_{A}\left( {A_{i,a} - A_{i,b}} \right)} - {w_{B}\left( {B_{i,a} - B_{i,b}} \right)} + {\ldots \mspace{11mu} {w_{N}\left( {N_{i,a} - N_{i,b}} \right)}}} \right)$

where Y_(i,a)−Y_(i,b) is the difference between average yields for thei-th set of adjacent pairs a and b. The system may use any knownminimization technique to compute the weights w_(A)−w_(N) that minimizethe above equation. The short length variability equation may then beused to identify short length variability where prior yield data isunavailable, but soil data is available for each cell.

In an embodiment, the system models short length variability as afunction of pixel values in satellite images of the field. For example,the system may receive satellite images of the agricultural field. Usingthe satellite images, the system may compute a value, such as an averagenormalized difference vegetation index (NDVI) value, for each grid cell.The system may then determine short length variability as the median ofthe differences between NDVI values between adjacent cells of aplurality of sets of adjacent cells. Additionally or alternatively,pixel values and/or values computed based on pixels values may be usedas an additional parameter in the above described modeling equations.

7.2. Selecting Fields Based on Short Length Variability

In an embodiment, the agricultural intelligence computer selects fieldsfor performing trials based on computed short length variability. Forexample, the agricultural intelligence computer system may receive arequest to generate prescription maps for a plurality of agriculturalfields that implement one or more trials. The agricultural intelligencecomputer system may use the methods described herein to compute theshort length variability for each agricultural field. The agriculturalintelligence computer system may then select an agricultural field forperforming a trial based on the short length variability. For instance,the agricultural intelligence computer system may select theagricultural field with the lowest short length variability of theplurality of agricultural fields.

In an embodiment, the agricultural intelligence computer systemadditionally computes a long length variability value. For example, foreach of a plurality of grid cells, the agricultural intelligencecomputer system may compute a difference between the average yield forthe grid cell and an average yield of the agricultural field containingthe grid cell. Additionally or alternatively, the agricultureintelligence computer system may model the long length variability as afunction of field values or image pixel values using any of the methodsdescribed in Section 7.1, but replacing the plurality of pairs ofadjacent grid cells with a plurality of pairs comprising a grid cell andaverages for the agricultural field.

The system may select agricultural fields with a low short lengthvariability score and a high long length variability score forperforming the trial. For example, the system may identify a pluralityof fields where the short length variability score is below a thresholdvalue and select from the identified plurality of fields theagricultural field with the highest long length variability score.Additionally or alternatively, the system may identify a plurality offields where the long length variability score is below a thresholdvalue and the select from the identified plurality of fields theagricultural field with the lowest short length variability value. Asanother example, the system may select the agricultural field with thehighest variability difference value, where the variability differencevalue is computed as:

V _(D) =αV _(L) −βV _(S)

where V_(d) is the variability difference value, V_(L) is the longlength variability value, V_(S) is the short length variability value,and a and are weights selected based on whether it is more important forthe trial for long length variability to be high or for short lengthvariability to be low.

7.3. Selecting and Sizing Testing Locations

In an embodiment, the system uses differences between adjacent locationsto select one or more pairs as testing locations for performing one ormore trials. For example, the system may compute a difference in averageyield for a plurality of pairs of adjacent grid cells or model adifference value between pairs of adjacent grid cells using any of themethods described herein. The system may then select N pairs of sets ofadjacent grid cells with the lowest computed or modeled differences forperforming a trial on the agricultural field.

The number N of trials may be predetermined and/or computed. Forexample, the agricultural intelligence computer system may receive arequest to generate a prescription map with a particular number oftrials. The agricultural intelligence computer system may then use themethods described herein to identify one or more fields and/or testinglocations for performing the trials. As another example, theagricultural intelligence computer system may compute the number oftesting locations as:

$N = \left( \frac{SNR*\sigma}{T} \right)^{2}$

where SNR is the signal-to-noise ration defined by a ratio between theaverage yield for each location and the short length yield variation, ais the standard deviation of the average yield difference betweenpotential testing locations, and T is the expected detectable treatmenteffect. Thus, if an experiment is expected to raise yield by 5 bushelsper acre, T would be 5.

In an embodiment, the system determines an area for performing thetrials in a manner that increases statistical significance of the trialwhile reducing the amount of area required to perform the trials. Forexample, the system may compute a trial size as:

A _(T)=2wb

where w is the width and b is a buffer size for the trial type. Thebuffer size refers to a spatial distance required for an agriculturalimplement to shift from one treatment type to the next. For example, thebuffer size for a planter may be 3 ft to indicate that it takes theplanter 3 ft to switch from one seeding population to a differentseeding population while the buffer size for nutrient application may be50 ft to indicate that it takes the implement 50 ft to switch from oneapplication amount of a nutrient to a second application amount.

In an embodiment, the above equation is also used to compute a gridoverlay size. For example, a first grid overlay may be used to determineshort length variability for a field. The system may then use the aboveequation to determine an optimal size for testing locations using theabove equation. The system may then generate a new grid overlay based onthe computed trial size. In an embodiment, the system pre-selects awidth of the grid cells based on a width of one or more agriculturalimplements and uses the pre-selected width and area to compute thelength of each grid cell.

7.4. Determining Testing Location Orientation

In an embodiment, the agriculture intelligence computer systemdetermines an orientation of the grid overlay and/or testing locationsbased on header information of one or more agricultural implements onthe agricultural field. For example, an agricultural implement maycontinually capture data identifying a direction of movement of theagricultural implement during one or more agricultural activities, suchas planting of a field, and send the captured data to the agriculturalintelligence computer system. The received directional data may includedirectional data related to turns at the ends of passes and directionaldata when the planter is moving both up and down the field.

In order to remove errors caused by the planted moving both up and downthe field, the system may identify directional data within a 180° arcand set each direction within the 180° arc to be the reverse of thatdirection. Thus, if 45% of the direction values for a planter indicatethat the planter is moving North and 45% of the direction values for theplanter indicate the planter is moving South, the agriculturalintelligence computer system may flip the South values so that 90% ofthe direction values for the planter indicate the planter is movingNorth. In order to remove directional data relating to turns at the endof passes, the agricultural intelligence computer system may select themedian direction of the directional data, thereby removing the numericaloutliers caused by turning of the agricultural equipment and movementaround trees and other obstacles.

In an embodiment, the agricultural intelligence computer systemidentifies locations where the planter has changed headings. Forexample, for a first portion of the field, the planter may plant at afirst angle and, for a second portion of the field, the planter mayplant at a second angle. In order to identify locations where theplanter has begun planting in a different direction, the agriculturalintelligence computer system may utilize a grouping algorithm toidentify locations where the values indicating direction of the planterhas changed.

In an embodiment, the agricultural intelligence computer systemdetermines that a change of direction has occurred when greater than athreshold number of sequential directional values identify a samedirection that is greater than a threshold number of degrees differentthan a previous direction. For example, if the planter generates a newdirectional value every 5 seconds, the system may determine that theplanter has begun planting in a new direction if more than 20 sequentialdirectional values are greater than 5° different from a prior determineddirection.

In an embodiment, the agricultural intelligence computer system usesimagery to determine a direction of the planter. For example, theagricultural intelligence computer system may identify straight lines inan aerial image of the agricultural field, such as on the boundaries ofthe agricultural field. The agricultural intelligence computer systemmay determine that the straight lines in the imagery correspond to adirection of the planting of the agricultural field and set the grid toline up with the identified direction.

7.5. Selecting from Grid Locations

In an embodiment, the agricultural intelligence computer system variesthe locations of cells within a grid to maximize a number of testinglocations that can be planted in an agricultural field. FIG. 22 depictsan example method of varying testing locations within a preset grid tomaximize a number of testing locations.

Map 2202 depicts a first map of a field with a grid overlay. In theexamples of FIG. 22, the vertical lines of the grid are fixed ascorresponding to a directional movement of the planter. Area 2204depicts a location with map 2202 which includes one complete grid celland two incomplete grid cells. In an embodiment, the agriculturalintelligence computer system identifies locations that includeincomplete grid cells. The agricultural intelligence computer system mayshift cells in the identified location in a single direction, such asthe direction of the planter, to fit more complete cells. For example,in map 2206, the cells in location 2208 have been shifted up. Whereas inmap 2202, only one complete cell fit in the location, in map 2206 twocells were able to fit in the same location 2208. Thus, in map 2210,both cells are capable of being used in different trials.

In an embodiment, agricultural intelligence computer system identifiesone or more incomplete cells in the grid. Agricultural intelligencecomputer system then determines which half of the cell comprises thelargest contiguous complete area from the boundary. For example, if acorner is missing from the top of the cell, but the bottom of the cellis intact, the system may identify the bottom portion of the cell as themost complete. The agricultural intelligence computer system may thenshift the cell and all cells affected by the shift in the direction ofthe most intact portion of the cell until a complete cell is made. Theagricultural intelligence computer system may then determine whether thecolumn containing the cell has a greater number of complete cells thanbefore. If the column contains a greater number of cells, the system maycontinue the process with the next incomplete cell in the column. Ifnot, the system may revert the column to its pre-shifted state andcontinue the process with the next incomplete cell in the column. Oncethe process has been performed with each incomplete cell in the column,the system may continue the process with the next column.

While the above methods are described in terms of field boundary, theymay also be utilized with respect to management zones. For example, acell may be considered incomplete if it comprises more than onemanagement zone. Thus, the system may shift cells up or down in order tomaximize a number of complete cells in a management zone. In anembodiment, the system first selects a smallest management zone andperforms the method described herein to increase a number of cells inthe smallest management zone. The system may then perform the method inthe next smallest management zone. After shifting cells in a managementzone, the system may additionally determine if the shift reduced anumber of complete cells in a previous management zone. If so, thesystem reverts the column to its pre-shifted state and continues theprocess with the next incomplete cell in the column.

In an embodiment, the system is able to shift cells such that twosequential cells are not abutting. For example, when a first cell isshifted down, the cell above the first cell may not be shifted. Thus,the system is able to shift cells around obstacles in the middle offields, such as small bodies of water and large trees while maximizingthe number of cells in the grid overlay.

While embodiments have been described using two adjacent cells, sometrials require use of more than two locations. For such locations, thesystem may identify clusters within a management zone for performing thetrial. The system may first select the smallest management zone, therebymaximizing the number of trials done in the smaller zones. The systemmay then randomly or pseudo-randomly select a first location. The systemmay then pseudo-randomly select second locations touching the firstlocation until all of the locations have been placed or no moresurrounding locations are available. If more locations need to beplaced, the system may randomly or pseudo-randomly select thirdlocations touching the second locations. The system may continue theprocess until all locations have been placed or no more locations can beplaced. If no more locations can be placed, the system may remove allprior placed locations and randomly or pseudo-randomly place a new firstlocation in the management zone to continue the process. If more than athreshold number of attempts to place a cluster of location have endedin failure, the system may then move to the next management zone.

7.6. Prescription Maps and Scripts

The methods described herein improve the process of the computer'sgeneration of prescription maps for performing one or more agriculturaltasks on an agricultural field. For example, the agriculturalintelligence computer system may receive a request to generate aprescription map for an agricultural field with one or more specifictrials. The agricultural intelligence computer system may use themethods described above to identify fields and testing locations,orientations of the testing locations, and sizes of the testinglocations. The agricultural intelligence computer system may thengenerate a prescription map which includes the trial being performed onthe testing locations. For example, if the trial is to double theseeding population, the agricultural intelligence computer system maygenerate the prescription map such that the seeing population for thetesting locations is double the population of the remaining locations.

In an embodiment, the agricultural intelligence computer system uses theprescription map to generate one or more scripts that are used tocontrol an operating parameter of an agricultural vehicle or implement.For example, the script may comprise instructions which, when executedby the application controller, cause the application controller to causean agricultural implement to apply a prescription to the field. Thescript may include a planting script, nutrient application script,chemical application script, irrigation script, and/or any other set ofinstructions used to control an agricultural implement.

8. Field Manager Computing Device Communication

The agricultural intelligence computing system may send the trialparticipation request to a graphical user interface on the field managercomputing device. The trial participation request may identify theconstraints of the trial and one or more values associated with thetrial. The value association is described further herein. The graphicaluser interface may include options for agreeing to participate in thetrial, selecting a particular amount of a field to dedicate to thetrial, selecting the degree of change in management practices, and/orselecting the desired confidence level of the results. The agriculturalintelligence computing system may identify possible locations in thefield for implementing testing locations for the trial. Additionally oralternatively, the graphical user interface may include options forselecting placement of the testing locations.

In an embodiment, the trial participation request does not directlyidentify a product or management practice to the field manager computingdevice. For example, the trial participation request may identify thatdifferent hybrid seeds are to be used in a trial location, but notidentify the type of hybrid seeds. The hybrid seeds may be physicallysent to the field for implementation of the trial. Thus, the fieldmanager may execute the trial without knowledge of the type of seedbeing planted, a type of product being applied, or one or moremanagement practices being applied as part of the trial.

FIG. 9 depicts a graphical user interface for selecting locations toplace testing locations. In the leftmost image of FIG. 9, the field isseparated into multiple zones based on soil type. In the rightmost imageshows application rates of nitrogen by location. One location has beenselected for implementing a testing location where nitrogen has beenapplied while a second location has been selected for implementing atesting location where nitrogen has not been applied, thereby acting asa control group. Both locations are within the same management zone.

The graphical user interface executing on the field manager computingdevice may include options for naming, describing, and tagging selectedlocations. FIG. 10 depicts an example graphical user interface fordefining selected locations. The display of FIG. 10 includes a text boxfor naming the selected location, a text box for adding a description ofthe selected location, and an option to select one or more tags for theselected location. The tags may be used later for searching throughprior selected locations. For instance, if the field manager implementsa plurality of different types of trials, the field manager may use thetags to identify locations that have been tagged for a particular typeof trial. While FIG. 10 is described in terms of a user interface,similar tags may be used by the agricultural intelligence computingsystem to track regions of the field with particular trials.

Once a region has been selected, the agricultural intelligence computingsystem may track and cause display of information pertaining to theselected region. FIG. 11 depicts an example graphical user interface fordisplaying information pertaining to a selected region. In FIG. 11, the“40 Lbs Control” region has been selected. The report depicts a yieldfor the location, a soil moisture of the location, as well as statisticsrelating to subregions of the selected region. For instance, the averageyield for the “Population>38.0 k seeds per acre” subregion is depictedunder the average yield for the selected location. In anotherembodiment, the report could depict yield for other locations, forinstance, the average yield for “Population>38.0 k seeds per acre” inthe trial region, or in the remainder of the field outside the “40 LbsControl” region.

The server may additionally display comparisons between trial data,control data, and other field data. FIG. 12 depicts an example graphicaluser interface for depicting results of a trial. FIG. 12 identifiesaverage yields for each type of trial as compared to the average yieldfor the field. The interface of FIG. 12 depicts example yields for thenitrogen control, nitrogen trial, and a late season nitrogen applicationtrial. The interface provides an easy visual verification of the effectson implementing the trial. A vertical line may also depict the averageyield for the entire field.

In an embodiment, the agricultural intelligence computing systeminitially tracks progress of implementing the testing locations withinthe field. For example, a field sensor may indicate where a fieldimplement has been planting crops or applying products. As the fieldimplement plants seeds within an area selected as a testing location,the agricultural intelligence computing system may monitor the plantingand/or applications in order to determine if the testing location is incompliance with requirements of the trial. For example, a trial mayrequire that a testing location include a requirement for a plantingpopulation of 35,000 seeds per acre. If the agricultural intelligencecomputing system receives data indication that an implement has planted35,000 seeds per acre in a particular testing location, the agriculturalintelligence computing system may indicate to the field manager that thetesting location has been correctly implemented. As an example, a colorof the testing location on a map displayed on the field managercomputing device may change in response to the server determining thatthe testing location meets the requirements of the trial.

As the server tracks the planting and/or application of a fieldimplement, the agricultural intelligence computing system may sendwarnings to a field manager computing device indicating that the fieldimplement is about to begin planting or application in a testinglocation. For example, the server may track the planting of a firsthybrid seed by a planting implement on a particular field. If thetesting location requires the planting of a second hybrid seed, theagricultural intelligence computing system may send a warning to thefield manager computing device as the planting implement nears thetesting location. The warning allows the field manager to stop theplanting implement before the planting implement invalidates the testinglocation for the trial. The warning may be any signal sent to the fieldmanager computing device, such as a pop-up notification, email, SMS/MMSmessage, or a signal that causes a light to flash on the field managercomputing device.

Additionally or alternatively, the agricultural intelligence computingsystem may send instructions that, if executed, cause a field implementto correct planting or applications in the testing location. Forexample, the agricultural intelligence computing system may send ascript that can be used to control a field implement to cause the fieldimplement to implement the trial. The agricultural intelligencecomputing system may send the script directly to a field managercomputing device controlling the implement, thereby automaticallycompensating for incorrect planting or applications. Additionally oralternative, the agricultural intelligence computing system may send thescript to a field manager computing device that is then used by a fieldmanager to compensate for planting or applications.

In an embodiment, the agricultural intelligence computing system offersalternatives if the agricultural intelligence computing systemdetermines that a testing location has been invalidated. When a testinglocation has been invalidated, the agricultural intelligence computingsystem may identify one or more additional locations for implementingthe testing location. The agricultural intelligence computing system maycause display, through the graphical user interface executing on thefield manager computing device, an identification of one or morealternative locations for implementing the testing location. In anembodiment, the graphical user interface may include options for thefield manager to select one of the alternative locations forimplementing the testing location. In another embodiment, theagricultural intelligence computing system may cause, through theapplication controller, the agricultural apparatus to automaticallyimplement the testing location at an alternative location withoutrequiring action from the field manager.

As an example, a first testing location may be defined at a firstlocation as a control group which does not receive a nitrogenapplication. If the agricultural intelligence computing systemdetermines that nitrogen has been applied to the first location, theagricultural intelligence computing system may identify one or moresecond locations where nitrogen has not been applied. The agriculturalintelligence computing system may cause display of the one or moresecond locations on the field manager computing device. In response toreceiving a selection of a particular location, the agriculturalintelligence computing system may update the map to indicate that theparticular location is a second testing location that is defined as acontrol group which does not receive a nitrogen application. Theagricultural intelligence computing system may then send warnings to thefield manager computing device to not apply nitrogen to the particularlocation.

As another example, a first testing location may be defined at a firstlocation as a control group which does not receive a nitrogenapplication. If the agricultural intelligence computing systemdetermines that nitrogen has been applied to the first location, theagricultural intelligence computing system may identify one or moresecond locations where nitrogen has not been applied. The agriculturalintelligence computing system may cause, directly through theapplication controller, the agricultural apparatus to automaticallyimplement the testing location at an alternative location withoutrequiring action from the field manager. The agricultural apparatus maythen not apply nitrogen to the particular location.

In an embodiment, the agricultural intelligence computing system may beprogrammed or configured to alter one or more trials in response todetermining that a testing location does not comply with a trial. In anembodiment, the agricultural intelligence computing system suggestsalterations to one or more practices for other locations to offseterrors in the testing location. For example, if a control location wasplanted with a seeding rate that is ten percent higher than required bythe trial, the agricultural intelligence computing system may modify theseeding rate for the other testing locations to be ten percent higher.

The agricultural intelligence computing system may additionally alterthe predicted results of the trial based on identified modifications tothe testing locations. For example, the agricultural intelligencecomputing system may predict an increase in yield of 30 bushels/acre foran application of 40 lbs/acre of nitrogen. If the agriculturalintelligence computing system detects that only 30 lbs/acre of nitrogenhas been applied to a field, the agricultural intelligence computingsystem may lower the predicted increase in yield of 30 bushels/acre.

The agricultural intelligence computing system may additionally useobserved field data to determine if a field is in compliance with atrial. For example, the agricultural intelligence computing system maycompare results of the trial to results of equivalent trials on otherfields and/or average results for a geographic region, such as a county.If the results of the trial vary widely from the results of the otherfield or geographic region, the agricultural intelligence computingsystem may determine that the trial was incorrectly implemented on thefield.

8. Value Association

In an embodiment, the agricultural intelligence computing systemassociates a result value with performance of the trial. The associatedresult value may be a reduced cost for obtaining products, a cost to thefield manager if the trial is successful, a rebate if the trial isunsuccessful, carbon credits, water use credits, and/or any form ofdigital currency.

In an embodiment, the trial participation request includes a commitmentto a particular outcome, such as an absolute yield, a revenue, a percentincrease of income or revenue based on yield, and/or a quality of thecrop. For example, the trial participation request may include aguarantee that the total yield for a field will increase by 20bushels/acre if a particular pesticide is used on the field. If thefield manager agrees to participate in the trial, the field manager isrequired to use the pesticide in one or more testing locations and notuse the pesticide in one or more control locations. If the testinglocation outperforms the control location by at least 20 bushels/acre,the agricultural intelligence computing system will determine that theguaranteed outcome has occurred. If the testing location does notoutperform the control location by at least 20 bushels/acre, theagricultural intelligence computing system may determine that theguaranteed outcome has not occurred.

In an embodiment, the trial participation request may offer a product orseed at a discount or for free in return for participation in the trialand a portion of profit if the guaranteed outcome occurs. For example, atrial participation offer may include free seeds of a particular hybridfor a farmer, but a promise that if the yield increase for the testinglocations exceed 20 bushels/acre, the field manager must pay ten percentof the increase in revenue and/or return on investment from the sale ofthe crop. The portion of profit may be a portion of actual profit ormodeled profit based on average prices for the harvested crop.

While embodiments have been described generally with respect to plantingof seeds or application of a product, a similar trial participationrequest may be based on different management practices. For example, theagricultural intelligence computing system may receive data from a fieldmanager computing device indicating historical management practices andhistorical yield. The agricultural intelligence computing system maycompute a benefit of changing one or more management practices. Theagricultural intelligence computing system may send a trialparticipation request that indicates that the agricultural intelligencecomputing system has identified one or more management practices which,if altered, would guarantee a particular benefit. If the field managercomputing device agrees to participate in the trial, the agriculturalintelligence computing system may send the one or more changedmanagement practices to the field manager computing device. If testinglocations that implement the changed management practices benefit by theguaranteed amount, the agricultural intelligence computing system mayrequest a portion of revenue and/or return on investment. For example,the agricultural intelligence computing system may compute a benefit ofchanging from fall nitrogen fertilizer application to spring nitrogenfertilizer application. If testing locations that implement the springnitrogen fertilizer application benefit by the guaranteed amount, theagricultural intelligence computing system may request a portion ofincreased revenue and/or return on investment.

While embodiments have been described generally with respect to plantingof seeds or application of a product or different management practices,a similar trial participation request may be based on different farmingequipment. For example, the agricultural intelligence computing systemmay receive data from a field manager computing device indicatinghistorical management practices, historical farming equipment, andhistorical yield. The agricultural intelligence computing system maycompute a benefit of changing one or more farming equipment pieces. Theagricultural intelligence computing system may send a trialparticipation request that indicates that the agricultural intelligencecomputing system has identified one or more farming equipment pieceswhich, if altered, would guarantee a particular benefit. If the fieldmanager computing device agrees to participate in the trial and thefarming equipment dealer agrees to participate in the trial, theagricultural intelligence computing system may send one or more changedmanagement practices to the field manager computing device and to thefarming equipment dealer. If testing locations that implement thechanged farming equipment benefit by the guaranteed amount, theagricultural intelligence computing system may request a portion ofrevenue and/or return on investment from the farm manager or from thefarming equipment dealer. For example, the agricultural intelligencecomputing system may compute a benefit of changing to new plantingequipment. If testing locations that implement the new plantingequipment benefit by the guaranteed amount, the agriculturalintelligence computing system may request a portion of increasedrevenue, return on investment, or equipment sale price.

Additionally or alternatively, the agricultural intelligence computingsystem may offer a rebate if the guaranteed increase in yield does notoccur. For example, the agricultural intelligence computing system maycharge for a particular product or for providing management practiceadvice. The agricultural intelligence computing system may guarantee aparticular increase in yield based on use of the provided managementpractice device or particular product. The agricultural intelligencecomputing system may additionally offer a rebate if the guaranteedparticular increase in yield does not occur. Thus, a field manager maybe assured that either the field manager will receive a substantialbenefit for participating in the trial or at least a portion of thecosts of participating in the trial will be recoverable.

In an embodiment, the agricultural intelligence computing systemdetermines the result value association based on captured data for thefield. For example, the agricultural intelligence computing system mayreceive field data including field descriptions, soil data, plantingdata, fertility data, harvest and yield data, crop protection data, pestand disease data, irrigation data, tiling data, imagery, weather data,and additional management data. Based on the field data, theagricultural intelligence computing system may compute benefits to thefield of using one or more products, management practices, farmingequipment, or seeds. The agricultural intelligence computing system maygenerate a trial participation request based on the computed benefits tothe field. For example, the agricultural intelligence computing systemmay be programmed or configured to offer the one or more products,management practices, farming equipment, or seeds at a particularpercentage of computed increase in profits for the field.

As an example, an agricultural intelligence computing system maydetermine that applying a particular management practice would increasethe yield of a field by 20 bushels/acre. The agricultural intelligencecomputing system may also determine that the price of the crop isroughly $4 per bushel. Thus, the expected increase in profit forimplementing the management practice would be $80/acre. If theagricultural intelligence computing system is programmed or configuredto request 10% of expected profits, the agricultural intelligencecomputing system may send a trial participation request that guaranteesan increase in yield of 15 bushels/acre at a cost of $8 per acreapplied.

In an embodiment, the agricultural intelligence computing systemdetermines the result value association based on a risk toleranceassociated with the field manager computing device. The risk tolerancemay be determined using any of the methods described herein. If the risktolerance associated with the field manager computing device is higherthan a particular value, the agricultural intelligence computing systemmay offer a relatively high initial price with a relatively high rebatefor failure to meet the condition. If the risk tolerance associated withthe field manager computing device is lower than a particular value, theagricultural intelligence computing system may offer a relatively lowinitial price with a relatively low rebate for failure to meet thecondition.

In an embodiment, the agricultural intelligence computing system sets aplurality of result values to be associated with the trial participationrequest. For example, the trial participation request may include atiered rebate system where a first rebate is paid out if the trialbenefited the yield, but not to the extent guaranteed by the trialparticipation request and a second rebate is paid out if the trial didnot benefit the yield. Other tier levels may be set based on the levelof benefit of the trial. For example, a tiered system may set differentrebate values for each 5 bushels/acre below the guaranteed yield.

Result value association may be based on individual trial locations oron a combination of trial locations. For example, the trialparticipation request may include an offer based on an averageperformance of all testing locations participating in the trial. Thus,one of the testing locations producing a yield lower than the guaranteedyield may not indicate a failure of the trial as long as the averageyield for the testing locations is above the guaranteed yield. Asanother example, the trial participation request may include an offerbased on an average performance of all testing locations from multipleoperations participating in the trial in a geographic region, like acounty.

In an embodiment, the trial participation request offer's region ofaverage performance used to determine the trial benefits may bedetermined based on a risk tolerance associated with the field managercomputing device. If the risk tolerance associated with the fieldmanager computing device is higher than a particular value, theagricultural intelligence computing system may offer a relatively smallregion of average performance, potentially subfield including to theindividual testing location level. If the risk tolerance associated withthe field manager computing device is lower than a particular value, theagricultural intelligence computing system may offer a relatively largeregion of average performance, potentially including testing locationsin fields spanning multiple field managers and even farming operationsacross a geographic area like a county.

In an embodiment, the result value association includes a guaranteedmargin for the field manager. For example, the agricultural intelligencecomputing system may model a likely yield and/or a likely revenue fromusing one or more seeds, one or more products, and/or one or moremanagement practices. The agricultural intelligence computing system mayguarantee a revenue for the field manager based on the modeled yieldand/or likely revenue. If the field manager computing device agrees tothe trial, the field manager may be provided with the one or more seeds,one or more products, and/or one or more management practices. Uponcompletion of the trial, the agricultural intelligence computing systemmay compute a result value comprising a difference between a predictedand/or actual revenue and the guaranteed revenue. The computed resultvalue may represent an amount due from the field manager. If thecomputed result value is negative, then the computed result valueindicates an amount owed to the field manager. Thus, the trialparticipation request is able to ensure a particular profit for thefield manager while still being beneficial for the trial requester.

In an embodiment, the associated result value may be based on a portionof the field assigned to the trial. For example, the agriculturalintelligence computing system may generate different levels of rebatesbased on the percentage or acreage of the field that the field manageragrees to use for the trial. A first rebate value may be set for a firstpercentage or amount of the field assigned to the trial and a secondhigher rebate value may be set for a second higher percentage or amountof the field assigned to the trial. Thus, the field manager isincentivized to increase the amount of the field dedicated to the trialin order to be able to claim the higher benefits and/or rebates.

9. Outcome Based Implementation

In an embodiment, the agricultural intelligence computer system isprogrammed to generate one or more value associations for a particulartrial recommendation. A trial recommendation may comprise one or moredifferent practices for a strict subset of an agronomic field or for anentire agronomic field. For example, the agricultural intelligencecomputer system may be programmed to recommend a different seed, adifferent seed population, a different fungicide application and/orapplication rate, a different crop protection practice, a differentherbicide application and/or application rate, a different fertilitypractice and/or fertility rate, and/or other different managementpractices for an agricultural field. The agricultural intelligencecomputer system may be programmed to use a modeled benefit ofimplementing a different seed and/or a different seed population togenerate one or more outcome-based values, such as a cost for seeds usedto perform the trial which is dependent on expected outcome.

In an embodiment, the agricultural intelligence computer system isprogrammed to receive field data for an agronomic field, the field dataidentifying past agronomic yield for one or more fields and one or morepreviously utilized management practices. The agricultural intelligencecomputer system is programmed to identify one or more differentmanagement practices corresponding to one or more products, such asplacement of different seeds at different seed densities. Using the oneor more different management practices, the agricultural intelligencecomputer system is programmed to create and store a digital modelcomprising executable instructions representing an improvement to theagronomic field of implementing the management practice. Theagricultural intelligence computer system is programmed to use themodeled improvements to generate one or more cost values for the one ormore products and displays the one or more cost values on a clientcomputing device.

If one of the one or more cost values are selected, the agriculturalintelligence computer system may be programmed to facilitateimplementation of the recommendation, such as through one or moreagricultural scripts that cause an agronomic machine to seed a field ata recommended seeding rate, and/or monitor an agronomic field todetermine that the field is in compliance with the recommendation. Forcost values that include a rebate based on a guaranteed yield, theagricultural intelligence computer system may be configured to onlyprovide the rebate if the agronomic field is in compliance with therecommendation.

9.1. Value Types

In an embodiment, the agricultural intelligence computer system computesa plurality of outcome-based values based, at least in part, on one ormore recommendations. For example, the agricultural intelligencecomputer system may compute a cost for seeds by the acre with no riskmanagement, a performance guarantee value, a performance matching value,and/or a profit matching value. While examples are described withrespect to changes in seed type and seeding rates, the methods describedherein may be utilized with changes in fungicide application,insecticide application, nutrient/nutrient inhibitor application, and/orother changes in management practice. Thus, while examples describedbelow use uplift in placement and density, other implementations mayinclude fungicide uplift, insecticide uplift, fertility uplift, and/oruplift due to any other management practice.

In an embodiment, the cost value per acre is computed as a function of amodeled benefit of performing one or more different managementpractices. For example, the price per acre based on a recommended seedtype and seed population may be computed as follows:

P _(A) =P _(Base)+(D _(up) +P _(up))*(B*I)

where B is a computed value per bushel of the planted crop, I is aselected percentage value, D_(up) is the density uplift, i.e. themodeled increase in yield due to implementing the different seedingpopulation, P_(up) is the placement uplift, i.e. the modeled increase inyield due to planting the recommended seed, and P_(Base) is a base valuefor the seed. The base value may be dependent on the modeled yield ofthe agronomic field with the planted seed, such as a modeled yield peracre multiplied by a seed cost, such as 60 cents.

The performance guarantee price may be computed in a similar manner withan added risk value to cover the guarantee. The guarantee may specify aparticular guaranteed yield. The agricultural intelligence computersystem may store a reimbursement value, indicating a reimbursementamount if the agricultural field does not receive the guaranteed yield.

The performance matching value may be computed in a similar way with alower risk value to cover the guarantee. The guarantee may specify aparticular guaranteed yield. The agricultural intelligence computersystem may additionally store a reimbursement value and overperformancepercentage. The overperformance percentage may comprise a percentage ofyield above the guaranteed yield which is computed using the methodsdescribed herein.

The profit matching value may be computed as a percentage of the modeledyield with an additional risk value. For example, the profit matchingvalue may be computed as:

P _(M) =P _(Base) *B*I+Risk

where the risk value is an additional set amount. The risk values may becomputed based on modeled information in order to cause the pay outvalues and the benefit values to average to zero. Additionally oralternatively, the risk value may be set values for each of theoutcome-based computations, regardless of the expected yield for theagricultural field.

9.2. Data Flow

FIG. 18 depicts an example data flow for producing one or moreoutcome-based values for a recommendation. The data flow of FIG. 18broadly describes an example process whereby field input data is used tocompute recommendations, offers, and guarantees. Other example processesmay use more or less data than described in FIG. 18 to compute one ormore of the outcome-based values described herein. All values referencedin FIG. 18 and in this description are digitally stored values capableof computer reading, writing and transformation under program control.The data may be prepared by the agricultural intelligence computersystem and/or received from one or more external server computers and/ordata sources, such as the provided data described in Section 4.

Raw grower data 1804 comprises raw data relating to a single grower. Rawgrower data 1804 may comprise a user identifier for the grower, anidentifier of one or more fields managed by the grower, a name of arecommended product, geographic region of the grower's fields, such asstate, county, latitude, longitude, and/or predefined zone, medianexpected yield based on yield model, density uplift, historical seedingdensity, recommended density, number of acres to which to apply therecommended density, historical yield for the grower's fields, placementuplift, variance of the placement uplift, yield probabilities, farmname, farm identifier, field name, and/or field identifier.

The yield probabilities may comprise a probability value for each of aplurality of yield values. For example, the agricultural intelligencecomputer system may compute an expected yield and yield variance for aparticular agricultural treatment. Based on the expected yield and yieldvariance, the agricultural intelligence computer system may determineprobabilities for each of a plurality of yield values, such as aprobability for each integer yield value between 1 and 500 bushels peracre.

The agricultural intelligence computer system aggregates raw grower data1804 into aggregated grower data 1808. The agricultural intelligencecomputer system may initially aggregate data for individual fields for aparticular grower into grower data for the particular grower, generatingvalues such as aggregated density uplift, aggregated placement uplift,and aggregated yield probabilities. Aggregated grower data 1808 maycomprise aggregations across a plurality of growers of actual productionhistory, density uplift, placement uplift, predicted yield, percentagesof different seed types used, and aggregated yield probabilities.

County zone data 1806 comprises general county and zone information,such as zone locations, county locations, states, and/or other generaldata. The zone information may be based on predefined areas referred toherein as “zones” generated by the agricultural intelligence computersystem and/or provided to the agricultural intelligence computer system.Bushel incremental value data 1814 may comprise data identifying arelative value of a bushel of the crop. For example the agriculturalintelligence computer system may receive previous pricing informationfor a particular crop at a particular region. The agriculturalintelligence computer system may use the previous pricing information tocompute a bushel incremental value indicating an expected price perbushel of the crop for the next season in the particular region.

The county zone data 1806 and bushel incremental value data 1814 may beused to generate zone data 1812. Zone data 1812 may comprise definitionsof one or more zones, average agronomic yield within the zone, averagecost of seeds in the zone, average bushel base price in the zone,average sale price of recommended seeds in the zone, as well as anyother aggregated values for one or more locations.

The aggregated grower data and zone data 1812 may be used to generatevalue association data 1820. For instance the agricultural intelligencecomputer system may determine values such as maximum likely yield for aparticular field based on the fields actual production history as wellas information regarding average yield for the zone. The valueassociation data may include an expected density uplift, placementuplift, bushel base price, yield guarantee, bushel value, incrementalvalue, and any other information used to generate a base price for oneor more seeding recommendations.

The agricultural intelligence computer system also may be programmed togenerate aggregated risk data 1810. Risk data generally relates to riskof loss due to failure of a field to meet the predicted yield.Aggregated risk data 1810 may comprise grower identifiers, operationidentifiers, location data for various fields, expected yields, actualyields, and yield probabilities.

The agricultural intelligence computer system may be programmed to usethe aggregated risk data 1810 and value association data 1820 togenerate risk coverage data 1816. Risk coverage data 1816 may comprise agrower identifier, product information, data identifying return due tooverperformance, a guarantee value, a risk adjustment value, a maximumpayout value, a profit sharing value, and/or a price floor value. Theoverperformance value may be computed as a percentage of yield greaterthan the guarantee such that the probabilities of each yield valuemultiplied by the expected payout or return and adjusted by the riskadjustment value average to zero. As an example, the agriculturalintelligence computer system may use the following equation to generaterisk guarantee values for each field and/or grower:

${{\sum\limits_{i = 1}^{500}{{{payout}\left( y_{i} \right)}*p_{y_{i}}}} - r} = 0$

such that the expected payout, computed as the payout value multipliedby the probability of that yield, minus the risk adjustment value equalszero.

The agricultural intelligence computer system may use the risk coveragedata 1816 to generate risk adjustments 1818 which comprise changes tothe value association data 1820 to account for various risks. The riskadjustments may include risk adjustment values, overperformance rates,rebate values, price floors, and price ceilings.

The agricultural intelligence computer system synthesizes the valueassociation data 1820 and the risk adjustments 1818 to generate a finaloutput 1822. Final output 1822 may comprise a file, such as a JSON file,which comprises values for each of the value types. For example, finaloutput 1822 may comprise a grower id, grower name, field data such asfield acreage, expected yield, and base price of the crop, a seed pricefor the per acre value, an expected yield for the per acre value, a seedprice for the performance guarantee, a guaranteed yield for theperformance guarantee, a rebate per bushel below the guaranteed yieldfor the performance guarantee, a minimum price for the performanceguarantee, a price for the performance matching value, a guaranteedyield for the performance matching value, a rebate per bushel below theguaranteed yield for the performance matching value, an overperformanceprice per bushel above the guaranteed yield for the performance matchingvalue, a minimum price for the performance matching value, a maximumprice for the performance matching value, a profit sharing percentagefor the profit sharing value, a minimum price for the profit sharingvalue, and/or a maximum price for the profit sharing value.

In an embodiment, the agricultural intelligence computer system displaysone or more outcome-based values on a client computing device. Forexample, the agricultural intelligence computer system may select anoffer to send to the client computing device. The selected offer may bebased on a risk tolerance value, such as the one described in Section 5,whereby the per acre value is selected for high risk tolerance and theperformance matching value is selected for low risk tolerance.Additionally or alternatively, the agricultural intelligence computersystem may display a plurality of outcome-based values on the clientcomputing device and receive input selecting a particular outcome-basedvalue.

9.3. Yield Modeling to Generate Guarantee Values

In an embodiment, the agricultural intelligence computer system uses aprobability density function to compute the probabilities of differentyields of an agricultural field. A probabilistic distribution indicatesnot just the likely yield values, but the probabilities of yield valuesbeing above or below a particular yield value. The agriculturalintelligence computer system may use the probability density functionsto compute a guaranteed yield value. For example, the agriculturalintelligence computer system may generate a probability density functionfor a particular field based on one or more data values corresponding tothe field. The agricultural intelligence computer system may select aparticular yield value from the probabilistic function such that thelikelihood of the yield being lower than the yield value based on theprobabilistic distribution is a particular percentage, such as 10%.Additionally or alternatively, the agricultural intelligence computersystem may compute probabilities of yield being within a particularrange, such as between 150-175 bu/ac. The agricultural intelligencecomputer system may select a range with a 90% likelihood of yield andcompute the guaranteed yield value as the bottom value of the range.

To use the probability distribution to generate the guaranteed yieldvalue, the agricultural intelligence computer system may generate theprobability density functions far in advance of current field or weatherconditions, such as a year prior to the planting of the agriculturalfield. Thus, the agricultural intelligence computer system may generatea model that is trained on input features which can be determined inadvance of the planting of a crop. For instance, the agriculturalintelligence computer system may receive previous yield data for one ormore fields, the previous yield data comprising an agronomic yield of afield planted with a particular hybrid seed, soil characteristic data,such as physical and/or chemical properties of the soil, field topologydata, field acreage, management practices, such as seeding rate andoccurrence or non-occurrence of crop rotation, and seed data, such astraits of the particular hybrid seed. The agricultural intelligencecomputer system may train the digital model using the soilcharacteristic data, field topology data, management practices, and seeddata as input and the yield as outputs.

The model may be a regression model, such as a generalized additivemodel (GAM), a tree-based model, a machine learning model, and/or aneural network model. The model may be configured to estimate adistribution, such as a sinh-arcsinh (SHASH) distribution.Alternatively, the agricultural intelligence computer system may usealternative methods of quantifying uncertainty, such as Monte Carlosampling. As an example, a four parameter sinh-arcsinh distribution maybe trained with one or more of the acreage of the field, crop rotation,seeding density, and hybrid seed traits as input parameters for one ormore of the tail parameter, shape parameter, skew parameter, and/orcenter parameter.

The probability distributions described herein may be used to generate aguaranteed value for a particular agronomic field planting a particularseed hybrid. In an example method, an agricultural intelligence computersystem receives past yield data for one or more fields, including pastyields and one or more past input features. The agriculturalintelligence computer system trains a digital model of crop yield, suchas a GAM, to predict parameters for a probability distribution of yield,such as a SHASH distribution. The agricultural intelligence computersystem may then use data relating to a particular agronomic field and aparticular crop hybrid to compute parameters for a SHASH distributionfor the particular field and crop hybrid. Using the SHASH distribution,the agricultural intelligence computer system may select a particularvalue as a guaranteed yield value.

The computation of a probabilistic distribution of yield as used hereinbenefits the agricultural intelligence computer system by giving theagricultural intelligence computer system access to field and hybridspecific data which would have been otherwise unavailable. For example,the probabilistic distributions generated using the models describedherein allow the agricultural intelligence computer system to select aguaranteed yield value based on a likelihood of yield being below orabove the selected value, thereby ensuring that yield guarantees sent tofield manager computing devices are high enough to depict an increase inyield for agronomic fields while being low enough that few fields willperform under the guarantees. By providing said guarantee values tofield manager computing devices, the agricultural intelligence computersystem uses input data to generate improved interfaces that allow fieldmanagers to make better decisions for planting agronomic fields.

9.4. Example Outcome-Based Display

FIG. 19 depicts an example outcome-based display. The outcome-baseddisplay of FIG. 19 comprises four outcome-based values, a seeds by theacre value, a performance guarantee value, a performance partner value,and a profit partner value. Each value comprises a different base seedprice which may be computed as described herein and an estimated yieldprojection based on an agronomic model. The values each also includeprice floors and price ceilings which indicate minimum and maximum seedprices.

The display of FIG. 19 allows a user to easily adjust values in order tomake an informed decision as to which value to select. For instance, thedisplay comprises a planned corn price in the top left corner which canbe adjusted through the graphical user interface. Additionally, thedisplay comprises a final bushel per acre slider that may be adjustedthrough the graphical user interface. In response to receiving anadjustment through the crop price option or final bushel per acreslider, the system may update the values in the display. For example,the crop revenue and total seed cost which includes a per bushel rebatemay be computed based on the crop price and the number of bushels peracre. As displayed in FIG. 19, the selected final bushel per acre valueof 200 is below the yield projection of 240. Thus, the total seed costfor the performance guarantee, performance partner, and profit partnervalues is adjusted to include rebates.

The ability of the display of FIG. 19 to update in response to changesin final output values allows for easy comparison between differenttypes of values. As the interface updates by recalculating crop revenue,total seed cost, and revenue less seed cost, a user is able to identifybenefits and risks with selecting different recommendations. Thus, theadaptive display of FIG. 19 uses the computing system to display largeamounts of information that otherwise would be difficult to compare,thereby allowing for a more informed decision to be made.

9.5. Example Outcome Based Trial Generation

FIG. 23 depicts an example method of implementing a trial on anagricultural field. For example, the agricultural intelligence computersystem may identify one or more trial recommendations for anagricultural field and send the one or more trial recommendations to afield manager computing device. If the agricultural intelligencecomputer system receives an acceptance of the trial, the agriculturalintelligence computer system may perform the method of FIG. 23 todetermine trial locations and analyze trial results. Other examples ofselecting locations for trials described in Sections 5 and 6 may beutilized in conjunction with or alternatively to the method described inFIG. 23.

At step 2302, a location for evaluating the trial is identified. Forexample, if the trial comprises a fungicide trial, the agriculturalintelligence computer system may identify a location for evaluating thetrial which includes a control location and a treatment location. In anembodiment, a location is identified on the field for placing threestrips of equivalent width, such as 240 ft wide. The outer two stripsmay comprise treatment locations while the inner strip comprises thecontrol location. The agricultural intelligence computer system maystore location data for a plurality of locations on the agriculturalfield. The agricultural intelligence computer system may additionallytag locations within the outer two strips as treatment locations andlocations within the inner strip as a control location.

In an embodiment, treatment locations and/or control locations may bedetermined based on data received from the field manager computingdevice. For example, the agricultural intelligence computer system mayreceive planting data from a field manager computing device whichincludes vehicle pass data identifying where a vehicle moved on theagricultural field, planting density data identifying a planting densityfor each location, and/or other data received from a planter or manuallyinput through a field manager computing device. Other examples of dataused may include soil data, previous yield data, application data, orother data relating to the agricultural field. Based on the receiveddata, the agricultural intelligence computer system may identifylocations where a trial can be performed.

The agricultural intelligence computer system may be configured toattempt to place strips in one or more optimal locations based on one ormore stored rules. For example, a stored rule may eliminate non-uniformareas, such as areas that were planted in separate passes or areas whichhave large obstructions. Another stored rule may eliminate areas under athreshold size. If the agricultural intelligence computer system isunable to select the center of the field, the agricultural intelligencecomputer system may be configured to select a location as close aspossible to the center of the field without violating any placementrules.

At step 2304, the trial is executed on the agricultural field. Forexample, the agricultural intelligence computer system may send data toa field manager computing device identifying the control strip, atreatment to be applied to the rest of the field, and a treatment orlack thereof to be applied to the control strip. As an example, for afungicide trial, the agricultural intelligence computer system mayidentify the fungicide to use on the agricultural field and a locationof the agricultural field that is to not receive fungicide as thecontrol group. The field manager computing device may send data to theagricultural intelligence computer system when an application has beenapplied to the agricultural field. For example an agricultural implementmay record location data when it sprays the field and send the locationdata to the agricultural intelligence computer system. The agriculturalintelligence computer system may then tag stored location data for eachlocation with data indicating whether the treatment was applied to thelocation.

At step 2306, a buffer is applied to the trial locations. For example,the agricultural intelligence computer system may generate a bufferbetween the control location and each treatment location. The buffersize may be dependent on a type of treatment and/or treatmentapplication. For instance, a seeding trial may have no buffer, afungicide trial utilizing a ground sprayer may have a 20 ft buffer, anda fungicide trial utilizing an aerial sprayer may have a 50 ft buffer.The agricultural intelligence computer system may tag stored locationdata for each location within the generated buffer with data indicatingthat the location is a buffer location.

At step 2308, quality control rules are applied to the trial locations.For example, the agricultural intelligence computer system maydetermine, based on received trial implementation data, which triallocations to exclude from analysis. The agricultural intelligencecomputer system may determine that particular trial locations are to beexcluded from analysis based on machine data received from anagricultural implement which executed the treatment of the trial. Theagricultural intelligence computer system may additionally oralternatively apply rules based on harvest data and/or machine data todetermine which locations to exclude from analysis. Examples includeedge passes, end rows, point rows, operational abnormalities, and/oryield outliers. The agricultural intelligence computer system may storedata indicating whether a location is an edge pass, end row, point row,operational abnormality, or yield outlier.

Edge passes may be identified as the edges of the agricultural field.For example, an edge pass may comprise a single machine pass around theedge of a field. Edge passes may be removed from evaluation as the outerrows have an advantage due to having access to greater resources.

End passes may be identified as locations in the field where anagricultural implement had to turn around for a next pass. End passesmay be removed from evaluation due to the disadvantages caused fromcompaction when the machine turns around and due to variations caused bythe machine accelerating and deceleration at the end passes.

Point rows may be identified as locations in the field where, based onmachine data, the agricultural intelligence computer system determinesthat a machine was not running at full capacity. For example, machinedata received by the agricultural intelligence computer system mayindicate, at each location, operational parameters of the machine, suchas how many rows are being planted, harvested, or otherwise treated.Each location where the machine data indicates that the machine wasoperating at less than full capacity, such as planting only 12 of 24rows or harvesting 6 of 8 rows, may be removed from evaluation, due tocompaction caused by increased passes.

Operational abnormalities may be identified as locations where a machinehad to deviate from a pass to go around an object, such as telephonepoles, above ground drains, power lines, sensors, or rocks. Operationalabnormality locations may be removed from evaluation as areas with largeobjects tend to receive less effective management due to lack ofaccessibility.

Yield outliers may be identified as locations where the yield value isinconsistent with other values of the field. As an example, theagricultural intelligence computer system may identify all locationswith a yield below a first threshold value, such as 0 bushels per acre,or with a yield above a second threshold value, such as 500 bushels peracre. As another example, the agricultural intelligence computer systemmay determine a distribution of yield values in each strip and removeyield values above three standard deviations or below three standarddeviations of the average yield values.

In an embodiment, yield outliers are identified based on spatialconsiderations. For example, the agricultural intelligence computersystem may generate a spatial model of the agricultural yield using anunderlying spatial smooth of the yield values in the field. For eachlocation, the agricultural intelligence computer system may compute anexpected yield at the location based on the spatial model. If thedifference between the actual yield and the expected yield is greaterthan a threshold value and/or if the actual yield is more than threestandard deviations from the expected yield, the agriculturalintelligence computer system may identify the location as a yieldoutlier.

At step 2310, trial performance is analyzed. For example, theagricultural intelligence computer system may determine an average yieldfor the treatment locations and an average yield for the controllocation and compare the two in order to compute an average uplift byusing the treatment. During analysis, the agricultural intelligencecomputer system may remove from the analysis any locations that havebeen tagged as buffers in step 2306 or that were identified to beremoved in step 2308. For example, the agricultural intelligencecomputer system may store data tags for one or more locations, such as atreatment tag for each location in a treatment strip, a control tag foreach location in the control strip, a buffer tag for each location inthe buffer, and so on. When computing average yield in the treatmentlocations, the agricultural intelligence computer system may onlyaggregate values from locations that are stored with the treatment tag,but not a buffer tag, edge pass tag, end pass tag, point row tag,abnormality tag, or outlier tag.

In an embodiment, the agricultural intelligence computer system stores aplurality of data layers for the agricultural field, each of the datalayers comprising spatial data for the field. FIG. 24 is an example ofdata layers that may be stored for an agricultural field. Yield layer2402 comprises spatial data identifying yields of locations in theagricultural field. The yields may be identified from machine data of aharvester implement. Buffer layer 2404 comprises spatial dataidentifying where a location is a buffer location or not. Treatmentlayer(s) 2406 comprises one or more spatial layers each identifyingwhether a treatment was applied to each location. The treatment layer(s)may be identified from machine data of a sprayer or other machine whichapplied the treatments. Experiment layer 2408 comprises spatial dataidentifying whether a location was selected as each a treatment locationor control location for the experiment. Quality control layer 2410comprises spatial data identifying whether a location was selected to beremoved from analysis, such as due to being part of an edge pass, endpass, point row, abnormality, or outlier. Planting data layer 2412comprises spatial data identifying hybrid type and/or seed density thatwas planted for each location.

In an embodiment, the agricultural intelligence computer systemdetermines, based on the plurality of data layers, which values to usein the analysis. For example, for the treatment calculation, theagricultural intelligence computer system may only select locations thatwere:

-   -   1. not identified as a buffer in buffer layer 2404;    -   2. identified as a treatment location in treatment layer(s)        2406;    -   3. identified as an experiment location in experiment layer        2408;    -   4. not identified as a location to be removed in quality control        layer 2410; and/or    -   5. identified as comprising the correct planting data, such as a        prescribed seeding density or hybrid in planting data layer        2412.

Similarly, for the control calculation, the agricultural intelligencecomputer system may select locations that follow the same criteria asabove except that they are not identified as a treatment location intreatment layer(s) 2406. By utilizing data layers, the agriculturalintelligence computer system can separately map the agricultural fieldbased on different criteria and use those maps to determine whichlocations can be used in evaluation of the trial. The agriculturalintelligence computer may additionally cause display, on a field managercomputing device, of an interface which depicts the average and/or totalyield of the treatment locations, the average and/or total yield of thecontrol locations, and/or an average and/or total yield increase fromusing the treatment.

9.6. Example Trial Recommendation Variation Implementation

In an embodiment, the agricultural intelligence computer system providesa variable trial recommendation to a client computing device. Thevariable trial recommendation may comprise one or more definedparameters, such as a particular seed to use or particular fungicide tospray, and one or more variable parameters. A variable parameter, asused herein, comprises a parameter that may be altered at the clientcomputing device. The agricultural intelligence computer system mayprovide limits to the alterations to the variable parameter to ensurethat a yield increase is obtained. For example, the agriculturalintelligence computer system may provide a graphical user interfacecomprising one or more options for a seeing rate for a particular hybridseed.

To determine which of a plurality of options for a variable parameter tomake available, the agricultural intelligence computer system mayleverage historical information relating to a grower's field andhistorical information relating to the trial recommendation, such asprevious trial information relating to a particular seed. The exampleimplementation described in this section relates to seeding density, butother implementations may relate to fungicide application amounts,nutrient application amounts, or other variable parameters.

Historical information relating to a trial recommendation may includetrial outcomes for different values of the variable parameter whilekeeping the defined parameters constant. For example, the agriculturalintelligence computer system may receive multi-year density responsetrial data for a plurality of hybrid seeds and a plurality of regions.The multi-year density response trial data may include, for each field,planting densities of a hybrid seed of the plurality of hybrid seeds,and corresponding yield values. As an example, for a particular hybridseed, a first year's trial data may include a planting density of200,000 seeds per acre with an average corn yield of 176 bushels peracre while a second year's trial data may include a planting density of220,000 seeds per acre with an average corn yield of 184 bushels peracre.

Using the historical information relating to the trial recommendation,the agricultural intelligence computer system may generate a pluralityof graphs depicting a relationship between the variable parameter and ayield value. The agricultural intelligence computer system may make adifferent graph for each yield environment planting a particular hybridseed. A yield environment, as used herein, refers to the differences inyield across different fields with the same planting parameters. Thus, ayield environment may be a particular field, a particular group oflocations on a field, and/or a grouping of fields and/or locations basedon similarities in yield response to the parameter. For example, ifmultiple fields are identified as being part of the same yieldenvironment, the agricultural intelligence computer system may use datafrom each of the multiple fields to make a single graph. The graphs maycomprise one or more curves created using the variable parameters for ayield location and their corresponding yields, such as throughpolynomial regression. As an example, a density curve may be generatedfrom a plurality of data points, each of which comprising a seedingdensity and a corresponding yield.

The agricultural intelligence computer system may use the plurality ofgraphs depicting a relationship between the variable parameter and theyield value to generate a plurality of interface element positions for agraphical user interface, such as based on slopes of the graphs and/orintercepts of the graphs. Additionally or alternatively, the interfaceelement positions may be received through user input. The interfaceelement positions, as used herein, refer to a plurality of values forthe variable parameter, each of the plurality of values corresponding toa different position on an interface element, such as a slider bar ordrop-down menu. For example, five seeding rates may be selected tocorrespond to five slider bar positions for a graphical user interface.The interface element positions may be generated generally for allfields and/or specifically for a group of fields, such as a geographicregion or yield environment, or for each agricultural field.

In an embodiment, the agricultural intelligence computer systemdetermines which of the plurality of interface element positions to makeavailable for a particular field manager computing device based on pastyield response data for one or more fields. FIG. 25 depicts an examplemethod for augmenting a graphical user interface based on past yieldresponse data for an agricultural field.

At step 2502, past yield response data is received for an agriculturalfield. The past yield response data may comprise, for each of one ormore years, one or more crop management values and a yield value. Theone or more crop management values may include a hybrid seed type, aseeding density, fungicide applications, nutrient applications, and/orother information relating to the management of a crop and/or field. Inan embodiment, the one or more crop management values include thevariable parameter and the defined parameter. As an example, past yieldresponse data for a particular year may include a hybrid seed type, aseeding density, and an agronomic yield for that year.

At step 2504, a likely yield environment is determined for theagricultural field. Determining the likely yield environment maycomprise comparing the past yield response data for the agriculturalfield to past yield response data for one or more other agriculturalfields. For example, the agricultural intelligence computer system mayinitially receive past yield response data for a plurality of differentagronomic fields, the past yield response data comprising yieldresponses for a particular hybrid seed with different seeding rates.Using the past yield responses, the agricultural intelligence computersystem may identify a plurality of different yield environments, each ofwhich comprising a computed relationship between seeding rate andagronomic yield, such as the density curves described above. Theagricultural intelligence computer system may compare the past yieldresponse data for the agricultural field to the plurality of differentyield environments, such as by computing a deviation of seeding andyield values from the density curves. For example, the agriculturalintelligence computer system may compute the deviation value for aparticular density curve as:

$D = {\sum\limits_{i = 1}^{n}\left( {Y_{C,s_{i}} - Y_{f,S_{i}}} \right)^{2}}$

where D is the deviation value, Y_(C,s) _(i) is the yield from thedensity curve at the seeding rate s_(i) and Y_(f,s) _(i) is the yieldfrom the past yield respond data for the agricultural field at theseeding rate. The agricultural intelligence computer system may selectthe yield environment with the lowest seeding rate.

In an embodiment, each yield environment may relate to a plurality ofdifferent defined parameter values, such as a plurality of hybrid seedtypes. For example, the agricultural intelligence computer system mayinitially receive past yield response data for a plurality of differentagronomic fields, the past yield responses comprising yield responseswith different hybrid seeds and different planting densities. Using thepast yield responses, the agricultural intelligence computer system mayidentify a plurality of different yield environments, each of whichcomprising a computed relationship between seeding rate and agronomicyield for a plurality of different defined parameters, such as thedensity curves described above. Thus, the agricultural intelligencecomputer system may select a yield environment for the agriculturalfield based on its past performance with a first hybrid seed type and,based on the identified yield environment, identify a density curve fora second hybrid seed type.

At step 2506, a variance range is computed for the likely yieldenvironment. The variance range, as used herein, refers to a range ofyield values around the likely yield environment for specific values ofthe variable parameter. For example, if the likely yield environmentcomprises a density curve for a particular seed hybrid, then thevariance range may comprise a range of values above and below thedensity curve at particular spots of the density curve, such as at thegrower's position on the density curve. The variance range may be a setvalue, such as +/−2 bushels per acre, or a percentage of the yield, suchas +/−1%.

At step 2508, each possible yield environment within the variance rangeis identified. For example, the agricultural intelligence computersystem may identify each yield environment with a density curve thatpasses through the variance range as a possible yield environment. Thus,if the variance range is set at +/−2 bushels per acre at the growersposition of 32,000 seeds per acre and a yield of 176 bushels per acre,then the agricultural intelligence computer system may select everydensity curve which, at 32,000 seeds per acre, passes through a yieldvalue between 174-176 bushels per acre.

At step 2510, valid interface element positions are determined, based onthe possible yield environments. For example, the agriculturalintelligence computer system may store upper and lower bound values foreach density curve. The agricultural intelligence computer system maythen select interface element positions which are within the upper andlower bounds of each density curve. For example, if positions 1, 2, and3 of the interface elements correspond to seeding rates of 32,000 seedsper acre, 34,000 seeds per acre, and 36,000 seeds per acre respectively,and the upper bounds of two density curves are 33,000 seeds per acre and35,000 seeds per acre, the agricultural intelligence computer system mayexclude positions 2 and 3 of the interface elements from the validinterface element positions, as the seeding rates corresponding topositions 2 and 3 are greater than an upper bound of at least one of thedensity curves.

FIG. 26 depicts an example of generating upper and lower bounds for adensity curve. Density curve 2602 comprises a density curve for a yieldenvironment corresponding to a particular agricultural field. Thedensity curve depicts a relationship between seeding rates and agronomicyields for the yield environment. A grower position 2604 is depicted onthe density curve based on the agricultural field's historical seedingrates. In some embodiments, the grower position 2604 may not correspondto previous yield rates for the agricultural field, such as when thehistorical seeding rates correspond to a different hybrid type than thedensity curve. The optimal point 2610 comprises a peak of the densitycurve and may be calculated as the point on the curve with a zero slope.

Lower bound 2606 may be computed based, at least in part, on growerposition 2604. For example, lower bound 2606 may be selected to overcomeinaccuracies in a yield monitor. Examples of computations for lowerbound 2606 may include a fixed addition to grower position 2604, such as5,000 seeds per acre greater than the grower's previous plantingdensity, or a percentage addition to grower position 2604, such as 2%increase in seeding rate from the grower's previous planting density.

Upper bound 2608 may be computed based on optimal point 2610 or growerposition 2604. For example, upper bound may be computed as a fixed orvariable percentage of seeding rate of optimal point 2610 or of growerposition 2604. As an example, upper bound 2608 may be computed as 90% ofthe seeding rate of optimal point 2610. Thus, if optimal point 2610 fora particular density graph exists at a seeding rate of 38,000 seeds peracre, the upper bound may be set at 34,200 seeds per acre (38,000×0.9).As another example, upper bound 2608 may be computed as a percentage ofthe seeding rate of grower position 2604, such as 120%. A variable upperbound may be set based on a grower density, such as applying a differentupper bound percentage for different ranges of grower densities. Forexample, an upper bound of 120% of the grower's seeding rate may be setfor seeding rates of 34,000-36,000 seeds per acre while an upper boundof 115% of the grower's seeding rate may be set for seeding rates of36,000-38,000 seeds per acre.

In an embodiment, the agricultural intelligence computer systemadditionally applies one or more rules to disqualify one or moreinterface element positions. For example, a rule may disqualify extremeseeding rate increases, such as removing any interface positions above afixed seeding rate increase from grower position 2604, such as anincrease of 5,000 seeds per acre or greater. As another example,interface positions may be disqualified if a seeding rate increase toreach the seeding rate of the interface positions exceeds a storedresponse range for a particular hybrid seed.

At step 2512, an interface element on a graphical user interface isaugmented to limit selection to one or more of the valid interfaceelement positions. Augmenting the graphical user interface may compriseremoving one or more options of the interface element. For example, if aslider bar originally has five positions, but only two positions wereidentified as valid in step 2510, the agricultural intelligence computersystem may augment the interface element to only include two positions.Additionally or alternatively, the agricultural intelligence computersystem may continue to display all five positions, but cause only thevalid two positions to be selectable. The other three positions may begraphically modified, such as grayed out, in order to visually indicatethat they are not selectable.

At step 2514, the agricultural intelligence computer system causesdisplay of the graphical user interface with the interface element withan option to select one of the valid interface element positions. Thegraphical user interface may be displayed as a part of a trialrecommendation. For example the agricultural intelligence computersystem may send data to a field manager computing device comprising arecommendation to plant a particular hybrid on one or more fields. Ifthe agricultural intelligence computer system receives data indicatingacceptance of the trial recommendation, the agricultural intelligencecomputer system may cause display of one or more graphical userinterfaces for modifying the trial within specified limits. One of themodifications may include a modified seeding rate selected through theinterface element of a displayed graphical user interface.

FIG. 27 depicts an example graphical user interface for modifying atrial. Interface 2700 comprises a trial adjustment interface, includingoptions to adjust one or more values relating to the trial. Trialinformation 2702 comprises implementation data for the trial. In FIG.27, trial information 2702 includes a plurality of different managementzones corresponding to mapped field 2704. For each management zone, apopulation rate, average yield, and management zone area is depictedbased on the current parameters of the trial recommendation.Additionally, trial information 2702 includes overall statistics, suchas estimated yield, seed cost per acre, and an estimated gross revenuebased on the estimated yield and an estimated price of the crop.

Interface 2700 further includes slider bar 2706. Slider bar 2706includes positions determined through the methods described herein.Thus, while interface 2700 in FIG. 27 includes five positions for sliderbar 2706, other interfaces provided to other field manager computingdevices may have less or more positions for slider bar 2706. In anembodiment, the agricultural intelligence computer system is configuredto update trial information 2702 in response to a selection of an optionin slider bar 2706. For example, the agricultural intelligence computersystem may compute one or more of the values in trial information 2702,such as the estimated yield, number of bags used, or estimated grossrevenue, using a seeding density determined by the slider bar. Theagricultural intelligence computer system may update trial information2702 displayed through interface 2700 when a different slider positionis selected.

The example trial recommendation variation techniques described hereinimprove the agricultural intelligence computer system by allowing thesystem to dynamically generate graphical user interfaces for differentfield manager computing devices based on particular fields and to adjustthe graphical user interfaces based on selections made through the fieldmanager computing device. Specifically, by dynamically altering theslider bar positions on the graphical user interface based on selectionsfrom the field manager computing device, the agricultural intelligencecomputer system is able to provide an interface which provides optionsbased on individual fields, instead of providing the same options toeach field manager computing device which could allow for selection ofless useful seeding rates.

9.7. Example Trial Based Outcome Communication Process

In an embodiment, the agricultural intelligence computer systemcommunicates with the field manager computing device through a dynamicgraphical user interface which updates based on selections from thefield manager computing device as well as tracked actions taken by oneor more agricultural implements. FIG. 28 comprises an example method forcommunicating with a field manager computing device regarding theimplementation of a trial subject to one or more rules.

At step 2802, field data is stored for an agronomic field. The fielddata may include identification of one or more fields managed by a userof the field manager computing device, acreage values for each of theone or more fields, location information, such as GPS coordinates, forthe one or more fields, previous planting data for the one or morefields including previous seed types, densities, and planting locations,and/or other data relating to physical properties of an agriculturalfield and/or previous agronomic practices on the agricultural field.

At step 2804, a trial recommendation is generated. The trialrecommendation may be generated using any of the methods describedabove. In an embodiment, the trial recommendation comprises arecommendation for one or more hybrid seeds to be planted on the one ormore agricultural fields. For example, the trial recommendation mayinclude different seeds to be planted on different fields. The trialrecommendation may additionally include required parameters, such as anumber of acres of the field to be planted according to the trial,locations to plant according to the trial, such as within specificboundaries, a number of acres and/or specific locations for planting acontrol group, seeding density values or ranges, and/or other requiredparameters for the trial.

At step 2806, the trial recommendation is displayed through a graphicaluser interface (GUI) on a field manager computing device. For example,the agricultural intelligence computer may supply a GUI to the fieldmanager computing device which can be used for reviewing trialrecommendations, agreeing to the trial recommendations, and/or reviewingthe status of a trial. The agricultural intelligence computer system mayprovide one or more trial recommendations through the GUI for selectionby a field manager computing device.

FIG. 29 depicts an example GUI displaying a plurality of trialrecommendations. Interface 2900 comprises a plurality of trialrecommendations, referred to in FIG. 29 as “Offers” for viewing by afield manager computing device. Each of the plurality of offerscomprises an offer name 2902, offer status 2904, and offer action 2906.Offers may be sent to the field manager computing device throughinterface 2900 in response to a request for an offer for one or morefields. Thus, the offer name may be specified by the field managercomputing device as part of the request or by the agriculturalintelligence computer system. The offer name 2902 further includes somefield information, such as the number of fields in the offer and thenumber of acres including in said fields.

Offer status 2904 comprises a current status of an offer. The status maybe dependent on actions taken by the field manager computing deviceand/or the agricultural intelligence computer system. For example, the“Enrolling Fields” status may be displayed after fields have beenselected but before a trial recommendation has been requested, the“Processing Recommendation” status may be displayed after a trialrecommendation has been requested but before it has been sent to thefield manager computing device, the “Portfolio Ready” status may bedisplayed after a trial recommendation has been sent to the fieldmanager computing device but prior to an acceptance of the trialrecommendation, and the “Pricing Ready” status may be displayed afterthe acceptance of the trial recommendation but prior to a selection ofthe outcome based value type.

Actions 2906 comprise selectable links which, when selected, causeperformance of an action relating to a corresponding trialrecommendation. For example, the “Request Recommendation” action maycause the field manager computing device to send a request for a trialrecommendation to the agricultural intelligence computer system, the“Cancel” action may cancel a request for a trial recommendation, the“Accept/Edit Portfolio” action may cause the GUI to shift to displayingan interface for reviewing or accepting a trial recommendation, and the“Select Price” action may cause the GUI to shift to displaying aninterface for selecting a particular outcome based value type.

FIG. 30 depicts an example GUI displaying a particular trialrecommendation. Interface 3000 comprises a plurality of recommendedseeds as part of the particular trial recommendation. In interface 3000,each of the plurality of recommended seeds is displayed withcorresponding information relating to the seed's traits, relativematurity, and required coverage of the field. Interface 3000 furtherincludes edit options 3002 and accept option 3004. Edit options 3002comprise options for editing a trial recommendation, such as by removinga recommended seed type. Other options for editing the trialrecommendation may be additionally displayed in interface 3000 or in oneor more other interfaces. The other options may include options forediting the seed density, options for adding or removing one or morefields, or other options for augmenting a trial recommendation. The editoptions 3002, when selected, may cause the field manager computingdevice to send one or more modifications of the trial recommendation tothe agricultural intelligence computer system. The accept option 3004,when selected, may cause the field manager computing device to send datato the agricultural intelligence computer system indicating acceptanceof the trial recommendation as displayed.

At step 2808, modifications of the trial recommendation are received.For example, the field manager computing device may send one or moremodifications to the agricultural intelligence computer system throughthe GUI displaying on the field manager computing device. Themodifications may include changes in the required parameters for thetrial recommendation, such as a removal of specific hybrid seeds,removals or additions of particular fields, changes in seeding densityor other augments to the trial recommendation.

At step 2810, the trial recommendation is updated and the system causesdisplay of the updated trial recommendation through the graphical userinterface. For example, the agricultural intelligence computer systemmay generate a new trial recommendation for the one or more fields usingthe modifications of the trial recommendations, such as a trialrecommendation without a removed seed hybrid or with an addedagricultural field. The agricultural intelligence computer system maygenerate the new trial recommendation using the systems and methodsdescribed herein and/or based on user input specifying acceptablechanges to the trial recommendation based on the modifications. The newtrial recommendation may be displayed through the GUI of FIGS. 29 and 30as described above. Steps 2808 and 2810 may not occur in some instances,such as when a trial is accepted by a field manager computing devicewithout modification.

At step 2812, the system receives a selection of the trial agreement andan outcome-based value. For example, the field manager computing devicemay receive a selection of the accept option 3004 of FIG. 30. Inresponse, the GUI may display one or more outcome-based value interfaceswith options for selection an outcome based value for the trialrecommendation. The agricultural intelligence computer system mayreceive the selection of the outcome-based value through the graphicaluser interface.

FIG. 31 depicts an example GUI displaying a comparison of outcome-basedvalues. Interface 3100 comprises selectable outcome-based tabs 3102,comparison information 3104, sale price option 3106, and yield bar 3108.Outcome based tabs 3102 comprise selectable tabs relating to differentoutcome-based values. The currently selected tab is the productcomparison tab, thereby causing interface 3100 to display comparisoninformation 3104. Comparison information 3104 comprises a plurality ofvalues corresponding to each of the outcome-based value types, such asthe seeds by acre value and the performance guarantee value. Comparisoninformation 3104 may be computed using the methods described herein. Forexample, the yield projection values and the yield guarantee values maybe computed based on a modeled yield and a modeled guarantee value asdescribed above.

Expected sale price 3106 comprises an expected value for selling thecrop planted on the one or more fields. The expected value may becomputed by the agricultural intelligence computer system based onprevious year's sales prices for the crop. Additionally oralternatively, the expected sale price may be a value selected throughthe graphical user interface, such as through a drop-down menu oreditable text box. In an embodiment, the agricultural intelligencecomputer system updates comparison information 3104 in response todetecting a change in the expected sale price 3106. For example, a croprevenue value may be computed as a function of a yield value and theexpected sale price 3106. In response to a change to the expected saleprice 3106, the agricultural intelligence computer system may recomputethe crop revenue value and update the graphical user interface todisplay the updated crop revenue value.

Yield bar 3108 comprises an interface element for selecting a yieldvalue for the one or more agronomic fields. While yield bar 3108 isdepicted as a slider bar in FIG. 31, in other embodiments yield bar 3108may be other interface elements, such as an editable text box ordrop-down menu. In response to the yield bar 3108 being used to select ayield value for the one or more agronomic fields, the agriculturalintelligence computer system may update comparison information 3104. Forexample, the seed cost values may be dependent on yield, such as withthe performance guarantee, performance partner, and profit partner yieldvalues. In response to detecting a change in the yield value, theagricultural intelligence computer system may update the seed costvalues and/or any other values dependent on the yield, such as croprevenue, and update the graphical user interface to display the updatedvalues.

The interface of FIG. 31 provides an improvement in interfaces forcomparing outcome-based values. The comparison information for each typeof outcome-based value can be updated with different sales prices andcrop yields. By updating information across multiple categories inresponse to changes in expected sale price and yield through interfaceelements, the agricultural intelligence computer system provides dynamiccontrols for comparing different types of outcome based values with fullknowledge of how those outcome based values would be affected by changesin sale price or changes in agronomic yield. Thus, the interface of FIG.31 improves the display of changing information relating to differenttrial types based on uncertainty in future values.

In response to receiving a selection of one of the selectableoutcome-based tabs 3102, the interface may display a specializedinterface for said selected outcome based tab. For example, in responseto receiving a selection of the “Seeds By Acre”, “PerformanceGuarantee”, “Performance Partner”, or “Profit Partner” tab, theagricultural intelligence computer system may cause display of aninterface relating to said selected outcome based value, such as theinterfaces of FIGS. 32, 33, 34, and 35 respectively.

FIG. 32 depicts an example GUI displaying information relating to the“Seeds By Acre” outcome based value. Interface 3200 includes trial terms3202, value calculator 3204, expected sale price 3206, yield bar 3208,and offer selection option 3210. Trial terms 3202 comprises the terms ofthe “Seeds By Acre” outcome-based value, such as the price for seeds andprojected yield. Value calculator 3204 comprises total values for theagronomic field given the trial terms 3202, an expected sale price inputinto expected sale price 3206, and/or a yield value input into yield bar3208. For the “Seeds by Acre” value, crop revenue and revenue less seedcost may be affected by values selected in expected sale price 3206and/or yield bar 3208. In response to receiving a selection of the offerselection option 3210, the agricultural intelligence computer system maydetermine that it has received a selection of the “Seeds By Acre”outcome based value for the trial recommendation.

FIG. 33 depicts an example GUI displaying information relating to the“Performance Guarantee” outcome-based value. Interface 3300 includestrial terms 3302, value calculator 3304, expected sale price 3306, yieldbar 3308, and offer selection option 3310. Trial terms 3302 comprisesthe terms of the “Performance Guarantee” outcome-based value, such asthe price for seeds and projected yield. Value calculator 3304 comprisestotal values for the agronomic field given the trial terms 3302, anexpected sale price input into expected sale price 3306, and/or a yieldvalue input into yield bar 3308. For the “Performance Guarantee” value,crop revenue and revenue less seed cost may be affected by valuesselected in expected sale price 3306 and/or yield bar 3308.Additionally, the refund value in the trial terms 3302 may also changeif a yield value is set below the yield guarantee value depicted onyield bar 3308. In response to receiving a selection of the offerselection option 3310, the agricultural intelligence computer system maydetermine that it has received a selection of the “PerformanceGuarantee” outcome-based value for the trial recommendation.

FIG. 34 depicts an example GUI displaying information relating to the“Performance Partner” outcome-based value. Interface 3400 includes trialterms 3402, value calculator 3404, expected sale price 3406, yield bar3408, and offer selection option 3410. Trial terms 3402 comprises theterms of the “Performance Partner” outcome-based value, such as theprice for seeds and projected yield. Value calculator 3404 comprisestotal values for the agronomic field given the trial terms 3402, anexpected sale price input into expected sale price 3406, and/or a yieldvalue input into yield bar 3408. For the “Performance Partner” value,crop revenue, total seed cost, and revenue less seed cost may beaffected by values selected in expected sale price 3406 and/or yield bar3408. Additionally, the refund value and share fee value in the trialterms 3402 may also change depending on a selected yield value on yieldbar 3408. In response to receiving a selection of the offer selectionoption 3410, the agricultural intelligence computer system may determinethat it has received a selection of the “Performance Partner”outcome-based value for the trial recommendation.

FIG. 35 depicts an example GUI displaying information relating to the“Profit Partner” outcome-based value. Interface 3500 includes trialterms 3502, value calculator 3504, expected sale price 3506, yield bar3508, and offer selection option 3510. Trial terms 3502 comprises theterms of the “Profit Partner” outcome-based value, such as the price forseeds and projected yield. Value calculator 3504 comprises total valuesfor the agronomic field given the trial terms 3502, an expected saleprice input into expected sale price 3506, and/or a yield value inputinto yield bar 3508. For the “Profit Partner” value, crop revenue andrevenue less seed cost may be affected by values selected in expectedsale price 3506 and/or yield bar 3508. In response to receiving aselection of the offer selection option 3510, the agriculturalintelligence computer system may determine that it has received aselection of the “Profit Partner” outcome-based value for the trialrecommendation.

Referring again to FIG. 28, at step 2814, trial agreement data isstored. The trial agreement data may include data identifying locationson the field subject to the trial agreement, data identifying a productto be planted on the field, data identifying one or more substituteproducts that may be planted on the field, data identifying one or moreseeding rates for the field, data identifying a selected outcome basedvalue, and any additional terms of the trial agreement, such asguaranteed yield values, price floor or ceiling values, or percentagesharing.

At step 2816, planting data is received. For example, an agriculturalimplement may monitor the planting of a crop, monitoring includingidentifying and storing location data with corresponding planting data,such as a seed type planted and planting density. The agriculturalimplement may send the planting data to an agricultural intelligencecomputer system. The planting data may comprise geospatial dataindicating seed types and/or planting densities for each location on theagronomic field.

At step 2818, planting reconciliation is performed. Plantingreconciliation, as used herein, refers to a process by which theagricultural intelligence computer system determines how much of theagronomic field has been planted according to the trial recommendation.Planting reconciliation may be performed by evaluating one or more ruleswith respect to the planting data, such as date rules, boundary rules,product rules, replanting rules, and/or density rules.

In an embodiment, the agricultural intelligence computer systemevaluates the one or more rules sequentially, with the outputs of eachrule evaluation comprising at least identifiers of locations that havebeen identified as reconcilable. Thus, if a location is identified asirreconcilable based on a first rule, the location may not be evaluatedfor any of the future rules. As an example, the agriculturalintelligence computer system may evaluate all planting data with respectto the date rules, evaluate reconcilable acres from the date rules withrespect to the boundary rules, evaluate reconcilable acres from theboundary rules with respect to the product rules, evaluate reconcilableacres from the product rules with respect to the replanting rules, andevaluate reconcilable acres from the replanting rules with respect tothe density rules.

Additionally, certain rules may be evaluated between rules based on theoutputs of those rules. For example, a rule may determine whether athreshold number of acres and/or percentage of the agronomic field isreconcilable from the output of particular rule. If the number of acresand/or percentage of the agronomic field that is reconcilable is notgreater than the threshold, the agricultural intelligence computersystem may determine that the agronomic field is not reconcilable andmay not process future rules.

Date rules, as used herein, refer to rules relating to a date ofplanting and/or to a date of upload of planting data. For example, theagricultural intelligence computer system may store an earliest plantingdate value and a field-specific late planting date value. All locationsplanted prior to the earlier planting date value or after thefield-specific planting date value may be identified as irreconcilableacres. As another example, the agricultural intelligence computer systemmay store a planting data upload date value. Any locations correspondingto planting data that was not uploaded prior to the planting data uploaddate value may be identified as irreconcilable acres.

Boundary rules, as used herein, refer to rules relating to boundaries ofthe agronomic field as identified in the stored trial agreement data.For example, the agricultural intelligence computer system may excludeplanted locations identified in the planting data from reconcilablelocations if they are outside the boundaries of the agronomic field orfields defined by the trial agreement data. In an embodiment, theagricultural intelligence computer system provides a margin of error,such as thirty-two feet, from the boundary such that locations slightlyoutside the boundary may be considered reconcilable in order to overcomedeviations in planting. The agricultural intelligence computer systemmay apply the margin of error only up to a point that the reconcilableplanted locations do not exceed a total area of planted locationsidentified in the trial agreement data. The agricultural intelligencecomputer system may reduce the reconcilable locations to a maximum ofthe contracted amount either while initially implementing the boundaryrules or when all other rules have been implemented. Thus, an output ofthe boundary rules may include an area that exceeds the size of the areaidentified by the trial agreement in case any future rules reduce thereconcilable area further. The agricultural intelligence computer systemmay generate an output from applying the boundary rules comprising atotal area of reconcilable locations, identifiers of reconcilablelocations, and data identifying any expanded boundaries generated whileimplementing the boundary rules.

Product rules, as used herein, refer to rules relating to a productplanted on the field. Product rules may include individual locationrules, such as a rule specifying that a location is reconcilable ifplanted with the identified product in the trial agreement andirreconcilable if unplanted, planted with a wrong crop type, or plantedwith neither the identified product or an identified substitute product.Additionally or alternatively, product rules may comprise rules relatingto multiple locations on a field. For example, a rule may state thatlocations planted with substitute products identified in the trialagreement data may not exceed the lesser of a specific size, such as 20acres, or a specific percentage of the one or more fields, such as 20%.product—reconcilable if primary hybrid or acceptable alternative. notreconcilable if unplanted, planted with wrong crop, or planted withcompetitor seed. In an embodiment, if the amount of the field plantedwith substitute product is greater than either of the specific size orthe specific percentage, the agricultural intelligence computer systemmay identify all acres planted with the substitute product asirreconcilable. Alternatively, the agricultural intelligence computersystem may only identify the less of 20 acres or 20% of the field thatis planted with the substitute product as reconcilable and identify theremainder as irreconcilable. In an embodiment, the agriculturalintelligence computer system only identifies locations planted with thesubstitute product as reconcilable if the identified product for thefield was planted on at least a portion of the field.

Replanting rules, as used herein, refer to rules relating to locationson the field that are replanted, such as in response to plantingmistakes or natural disasters. For example, a replanting rule mayspecify that a location is reconcilable if the location contained areconcilable product prior to the replant and a reconcilable productafter the replant.

Density rules, as used herein, refer to rules relating to a seedingdensity on the agricultural field. The seeding density rules may statethat locations are reconcilable as long as the average seeding densityof the locations are within a particular range around the seedingdensity identified in the trial agreement data. For example, a maximumnumber of locations on the field may be selected with the limitationthat the average seeding density is within a range of −3% to +6% of theidentified density in the trial agreement data. The range may bedependent on the type of product planted. Thus, if multiple products areplanted on the field, the range may be evaluated with respect to anacre-weighted average for each product or planting instance.

At step 2820, harvest data is received. For example, an agriculturalimplement may monitor the harvesting of a crop, monitoring includingidentifying and storing location data with corresponding harvestingdata, such as yield values corresponding to harvested locations. Theagricultural implement may send the harvest data to an agriculturalintelligence computer system. The harvest data may comprise geospatialdata indicating yield values for each location on the agronomic field.

At step 2822, harvest reconciliation is performed. Harvestreconciliation, as used herein, refers to a process by which theagricultural intelligence computer system determines how much of thereconcilable portions of the agronomic field has been harvested. Thus,the agricultural intelligence computer system may cross-reference thelocations that were identified as reconcilable in step 2818 with thelocations identified as harvested in the harvest data to determine howmuch of the field is reconcilable.

Based on reconciliation data identified in step 2822 or 2818, theagricultural computer system may determine whether the outcome-basedvalue is still viable for the agronomic field. For example, theagricultural intelligence computer system may store threshold values forone or more of the outcome-based values indicating a minimum number ofacres and/or minimum percentage of the agronomic field that must bereconcilable for the outcome based value to be viable. If the number ofacres or percentage of the agronomic field that is reconcilable is lessthan the threshold value, the agricultural intelligence computer systemmay change the outcome-based value, such as to the Seeds By Acre value.The agricultural intelligence computer system may be configured to senda notification to a field manager computing device in response todetermining that the number of acres or percentage of the field that isreconcilable is less than the threshold value.

In an embodiment, the agricultural intelligence computer system displaysa harvest reconciliation interface identifying which identifiespercentages of the agricultural field that are reconcilable,irreconcilable, or unplanted. The interface may additionally identifysources of irreconcilable locations, such as locations outside of aboundary on a map or locations planted after the planting date. In anembodiment, after the harvest reconciliation, the agriculturalintelligence computer system computes a final outcome-based value forthe agronomic field based on the reconcilable locations, the yield forthe reconcilable locations, and the outcome based value type selectedfor the agronomic field.

10. Benefits of Certain Embodiments

Using the techniques described herein, a computer can track practicesacross a plurality of fields, identify fields that would benefit fromperforming a trial, identify locations for performing trials, andincentivize participation in the trials. The techniques described hereinmay additionally be used to automate machinery on a particular field.For example, upon determining a testing location on a field andreceiving from the field manager computing device an agreement toparticipate in the trial, the agricultural intelligence computing systemmay generate one or more scripts for field implements that cause thefield implements to plant seeds, apply products, or perform particularmanagement practices in accordance with the trial. Additionally, bymonitoring field implements in real-time, an agricultural intelligencecomputing system may be able to identify incorrect applications beforethey occur and/or identify alternatives in response to an incorrectapplication. Thus, the methods described herein may improve theagricultural intelligence computing system's ability to interact withthe field manager computing device over a network and provide real-timesolutions.

11. Extensions and Alternatives

In the foregoing specification, embodiments have been described withreference to numerous specific details that may vary from implementationto implementation. The specification and drawings are, accordingly, tobe regarded in an illustrative rather than a restrictive sense. The soleand exclusive indicator of the scope of the disclosure, and what isintended by the applicants to be the scope of the disclosure, is theliteral and equivalent scope of the set of claims that issue from thisapplication, in the specific form in which such claims issue, includingany subsequent correction.

What is claimed is:
 1. A computer system comprising: one or more processors; a digital electronic memory coupled to the one or more processors and storing instructions which, when executed by the one or more processors, cause performance of: receiving field data for a particular agricultural field; generating a trial recommendation for the particular agricultural field, the trial recommendation comprising one or more management practices for the particular agricultural field; using the field data for the particular agricultural field, computing a plurality of yield probabilities for the particular agricultural field, each of the yield probabilities comprising a probability of the particular agricultural field producing a different yield value when implementing the trial recommendation; using the plurality of yield probabilities generating a plurality of outcome-based values for the particular agricultural field; generating and causing displaying a graphical user interface in a computer display device, the graphical user interface visually displaying each of the plurality of outcome-based values for the particular agricultural field, the graphical user interface also visually displaying a final bushel per acre slider widget that is programmed to generate different values in response to interactive sliding of the widget; receiving user input selecting a particular value via the final bushel per acre slider widget and, in response, computing a crop revenue value for each of the outcome-based values and based on the particular value; causing displaying the crop revenue value for each of the outcome-based values in the graphical user interface.
 2. The system of claim 1, the plurality of outcome-based values comprising a performance guarantee value, the performance guarantee value comprising an implementation cost, a guaranteed yield value, and a reimbursement value indicating a reimbursement amount if the particular agricultural field does not produce a yield of the guaranteed yield value.
 3. The system of claim 1, the plurality of outcome-based values comprising a performance matching value, the performance matching value comprising an implementation cost, a guaranteed yield value, a reimbursement value indicating a reimbursement amount when the particular agricultural field does not produce a yield of the guaranteed value, and an overperformance percentage comprising a percentage of any yield produced by the particular agricultural field.
 4. The system of claim 1, wherein the instructions, when executed by the one or more processors, further cause performance of: receiving a selection of a particular outcome-based value of the plurality of outcome-based values; receiving application data for the particular agricultural field; based, at least in part, on the application data, determining that the particular agricultural field is in compliance with the particular trial; receiving yield values for the particular agricultural field; based, at least in part, on the particular outcome-based value and the yield values, computing a benefit value for the particular trial.
 5. The system of claim 4, wherein the instructions, when executed by the one or more processors, further cause performance of: generating, based on the application data for the particular agricultural field, a plurality of data layers for the agricultural field comprising a buffer layer, a treatment layer, a quality control layer and a planting data layer; determining that the particular agricultural field is in compliance with the trial by evaluating the application data with respect to the plurality of data layers for the agricultural field.
 6. The system of claim 5, wherein the quality control layer identifies one or more of edge passes, end passes, point rows, or operational abnormalities.
 7. The system of claim 1, wherein generating the trial recommendation for the one or more fields comprises computing a short length variability for the agricultural field and identifying locations for implementing the trial based, at least in part, on the short length variability for the agricultural field.
 8. The system of claim 1, wherein the instructions, when executed by the one or more processors, further cause performance of: using previous yield data for a plurality of agricultural fields, training a digital model of crop yield to predict parameters for a probability distribution of yield; using previous yield data for the particular agricultural field, computing parameters for the probability distribution of yield for the agricultural field from the trained digital model of crop yield; computing the plurality of yield probabilities from the probability distribution of yield for the agricultural field.
 9. The system of claim 1, wherein the instructions, when executed by the one or more processors, further cause performance of: receiving past yield response data for the particular agricultural field; determining a likely yield environment for the particular agricultural field; computing a variance range for the likely yield environment; identifying each possible yield environment within the variance range; based on each possible yield environment, determining one or more valid interface element positions for an interface element on the graphical user interface; augmenting the graphical user interface to limit selection of positions on the interface element to one of the one or more valid interface positions; causing display of the interface element on the graphical user interface with an option to select one of the one or more valid interface positions.
 10. The system of claim 1, wherein the one or more management practices for the particular agricultural field differ from one or more previous management practices for the particular agricultural field.
 11. A computer-implemented method comprising: receiving, at an agricultural intelligence computing system, field data for a particular agricultural field; generating a trial recommendation for the particular agricultural field, the trial recommendation comprising one or more management practices for the particular agricultural field; using the field data for the particular agricultural field, computing a plurality of yield probabilities for the particular agricultural field, each of which the yield probabilities comprising a probability of the particular agricultural field producing a different yield value when implementing the trial recommendation; using the plurality of yield probabilities generating a plurality of outcome-based values for the particular agricultural field; generating and causing displaying a graphical user interface in a computer display device, the graphical user interface comprising visually displaying each of the plurality of outcome-based values for the particular agricultural field, the graphical user interface comprising also visually displaying a final bushel per acre slider widget that is programmed to generate different values in response to interactive sliding of the widget; receiving user input selecting a particular value on via the final bushel per acre slider widget and, in response, computing a crop revenue value for each of the outcome-based values, a crop revenue value and based on the particular value; causing displaying the crop revenue value for each of the outcome-based values through in the graphical user interface.
 12. The method of claim 11 wherein the plurality of outcome-based values comprise a performance guarantee value, the performance guarantee value comprising an implementation cost, a guaranteed yield value, and a reimbursement value indicating a reimbursement amount if the particular agricultural field does not produce a yield of the guaranteed yield value.
 13. The method of claim 11 wherein the plurality of outcome-based values comprises a performance matching value, the performance matching value comprising an implementation cost, a guaranteed yield value, a reimbursement value indicating a reimbursement amount when the particular agricultural field does not produce a yield of the guaranteed value, and an overperformance percentage comprising a percentage of any yield that was produced by the particular agricultural field.
 14. The method of claim 11, further comprising: receiving a selection of a particular outcome-based value of the plurality of outcome-based values; receiving application data for the particular agricultural field; based, at least in part, on the application data, determining that the particular agricultural field is in compliance with the particular trial; receiving yield values for the particular agricultural field; based, at least in part, on the particular outcome-based value and the yield values, computing a benefit value for the particular trial.
 15. The method of claim 14, further comprising: generating, based on the application data for the particular agricultural field, a plurality of data layers for the agricultural field comprising a buffer layer, a treatment layer, a quality control layer and a planting data layer; determining that the particular agricultural field is in compliance with the trial by evaluating the application data with respect to the plurality of data layers for the agricultural field.
 16. The method of claim 15, wherein the quality control layer identifies one or more of edge passes, end passes, point rows, or operational abnormalities.
 17. The method of claim 11, wherein generating the trial recommendation for the one or more fields comprises computing a short length variability for the agricultural field and identifying locations for implementing the trial based, at least in part, on the short length variability for the agricultural field.
 18. The method of claim 11, further comprising: using previous yield data for a plurality of agricultural fields, training a digital model of crop yield to predict parameters for a probability distribution of yield; using previous yield data for the particular agricultural field, computing parameters for the probability distribution of yield for the agricultural field from the trained digital model of crop yield; computing the plurality of yield probabilities from the probability distribution of yield for the agricultural field.
 19. The method of claim 11, further comprising: receiving past yield response data for the particular agricultural field; determining a likely yield environment for the particular agricultural field; computing a variance range for the likely yield environment; identifying each possible yield environment within the variance range; based on each possible yield environment, determining one or more valid interface element positions for an interface element on the graphical user interface; augmenting the graphical user interface to limit selection of positions on the interface element to one of the one or more valid interface positions; causing display of the interface element on the graphical user interface with an option to select one of the one or more valid interface positions.
 20. The method of claim 11, wherein the one or more management practices for the particular agricultural field differ from one or more previous management practices for the particular agricultural field. 